examples: added Andriy's examples and bug fixes

src/Examples/ConsoleHelper.cs

@@ -0,0 +1,109 @@
+// <copyright file="ConsoleHelper.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples
+{
+    using System;
+    using MathNet.Numerics.Statistics;
+
+    /// <summary>
+    /// Helper fucntions to output into Console window
+    /// </summary>
+    public static class ConsoleHelper
+    {
+        /// <summary>
+        /// Disoplay histogram from the array
+        /// </summary>
+        /// <param name="data">Source array</param>
+        public static void DisplayHistogram(double[] data)
+        {
+            var blockSymbol = Convert.ToChar(9608);
+
+            var rowMaxLength = Console.WindowWidth - 1;
+            rowMaxLength = (rowMaxLength / 10) * 10;
+            var rowCount = rowMaxLength / 3;
+
+            var histogram = new Histogram(data, rowMaxLength);
+
+            // Find the absolute peak 
+            var maxBucketCount = 0.0;
+            for (var i = 0; i < histogram.BucketCount; i++)
+            {
+                if (histogram[i].Count > maxBucketCount)
+                {
+                    maxBucketCount = histogram[i].Count;
+                }
+            }
+
+            // Number of bucket counts between rows
+            var rowStep = maxBucketCount / rowCount;
+
+            // Draw histogram line-by-line
+            Console.WriteLine();
+
+            for (var row = 0; row < rowCount; row++)
+            {
+                for (var col = 0; col < histogram.BucketCount; col++)
+                {
+                    if (histogram[col].Count >= maxBucketCount)
+                    {
+                        Console.Write(blockSymbol);
+                    }
+                    else
+                    {
+                        Console.Write(@" ");
+                    }
+                }
+
+                Console.SetCursorPosition(0, Console.CursorTop + 1);
+                maxBucketCount -= rowStep;
+            }
+
+            // Calculate distanse between label in X axis
+            var axisStep = histogram.BucketCount / 2;
+
+            var leftLabel = histogram.LowerBound.ToString("N");
+            var middleLabel = ((histogram.UpperBound + histogram.LowerBound) / 2.0).ToString("N");
+            var rightLabel = histogram.UpperBound.ToString("N");
+
+            Console.Write(leftLabel);
+            for (var j = 0; j < axisStep - leftLabel.Length; j++)
+            {
+                Console.Write(@" ");
+            }
+
+            Console.Write(middleLabel);
+            for (var j = 0; j < axisStep - middleLabel.Length; j++)
+            {
+                Console.Write(@" ");
+            }
+
+            Console.Write(rightLabel);
+
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/ContinuousDistributions/BetaDistribution.cs

@@ -0,0 +1,165 @@
+// <copyright file="BetaDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Beta distribution example
+    /// </summary>
+    public class BetaDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/BetaDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Beta distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Beta distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Beta_distribution">Beta distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Beta distribution class with parameters a = 5 and b = 1.
+            var beta = new Beta(5, 1);
+            Console.WriteLine(@"1. Initialize the new instance of the Beta distribution class with parameters a = {0} and b = {1}", beta.A, beta.B);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", beta);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", beta.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", beta.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", beta.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", beta.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", beta.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", beta.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", beta.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", beta.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", beta.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", beta.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", beta.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Beta distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Beta distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(beta.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Beta(5, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Beta(5, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = beta.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Beta(2, 5) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Beta(2, 5) distribution and display histogram");
+            beta.A = 2;
+            beta.B = 5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = beta.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Beta distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Beta(0.5, 0.5) distribution and display histogram");
+            beta.A = 0.5;
+            beta.B = 0.5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = beta.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 7. Generate 100000 samples of the Beta distribution and display histogram
+            Console.WriteLine(@"7. Generate 100000 samples of the Beta(2, 2) distribution and display histogram");
+            beta.A = 2;
+            beta.B = 2;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = beta.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/CauchyDistribution.cs

@@ -0,0 +1,108 @@
+// <copyright file="CauchyDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Cauchy distribution example
+    /// </summary>
+    public class CauchyDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/CauchyDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Cauchy distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Cauchy distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Cauchy_distribution">Cauchy distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Cauchy distribution class with parameters Location = 1 and Scale = 2.
+            var cauchy = new Cauchy(1, 2);
+            Console.WriteLine(@"1. Initialize the new instance of the Cauchy distribution class with parameters Location = {0} and Scale = {1}", cauchy.Location, cauchy.Scale);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", cauchy);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", cauchy.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", cauchy.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", cauchy.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", cauchy.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", cauchy.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", cauchy.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", cauchy.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", cauchy.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // 3. Generate 10 samples of the Cauchy distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Cauchy distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(cauchy.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/ContinuousDistributions/ChiDistribution.cs

@@ -0,0 +1,151 @@
+// <copyright file="ChiDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Chi distribution example
+    /// </summary>
+    public class ChiDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/ChiDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Chi distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Chi distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Chi_distribution">Chi distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Chi distribution class with parameter dof = 1.
+            var chi = new Chi(1);
+            Console.WriteLine(@"1. Initialize the new instance of the Chi distribution class with parameter DegreesOfFreedom = {0}", chi.DegreesOfFreedom);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", chi);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", chi.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", chi.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", chi.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", chi.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", chi.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", chi.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", chi.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", chi.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", chi.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", chi.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", chi.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Chi distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Chi distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(chi.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Chi(1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Chi(1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = chi.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Chi(2) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Chi(2) distribution and display histogram");
+            chi.DegreesOfFreedom = 2;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = chi.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Chi(5) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Chi(5) distribution and display histogram");
+            chi.DegreesOfFreedom = 5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = chi.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/ChiSquareDistribution.cs

@@ -0,0 +1,154 @@
+// <copyright file="ChiSquareDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// ChiSquare distribution example
+    /// </summary>
+    public class ChiSquareDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/ChiSquareDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "ChiSquare distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "ChiSquare distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Chi-square_distribution">ChiSquare distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the ChiSquare distribution class with parameter dof = 1.
+            var chiSquare = new ChiSquare(1);
+            Console.WriteLine(@"1. Initialize the new instance of the ChiSquare distribution class with parameter DegreesOfFreedom = {0}", chiSquare.DegreesOfFreedom);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", chiSquare);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", chiSquare.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", chiSquare.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", chiSquare.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", chiSquare.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", chiSquare.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", chiSquare.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", chiSquare.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", chiSquare.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", chiSquare.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", chiSquare.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", chiSquare.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", chiSquare.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the ChiSquare distribution
+            Console.WriteLine(@"3. Generate 10 samples of the ChiSquare distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(chiSquare.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the ChiSquare(1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the ChiSquare(1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = chiSquare.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the ChiSquare(4) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the ChiSquare(4) distribution and display histogram");
+            chiSquare.DegreesOfFreedom = 4;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = chiSquare.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the ChiSquare(8) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the ChiSquare(8) distribution and display histogram");
+            chiSquare.DegreesOfFreedom = 8;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = chiSquare.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/ContinuousUniformDistribution.cs

@@ -0,0 +1,144 @@
+// <copyright file="ContinuousUniformDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// ContinuousUniform distribution example
+    /// </summary>
+    public class ContinuousUniformDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/UniformDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "ContinuousUniform distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "ContinuousUniform distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29">ContinuousUniform distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the ContinuousUniform distribution class with default parameters.
+            var continuousUniform = new ContinuousUniform();
+            Console.WriteLine(@"1. Initialize the new instance of the ContinuousUniform distribution class with parameters Lower = {0}, Upper = {1}", continuousUniform.Lower, continuousUniform.Upper);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", continuousUniform);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", continuousUniform.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", continuousUniform.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", continuousUniform.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", continuousUniform.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", continuousUniform.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", continuousUniform.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", continuousUniform.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", continuousUniform.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", continuousUniform.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", continuousUniform.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", continuousUniform.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", continuousUniform.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the ContinuousUniform distribution
+            Console.WriteLine(@"3. Generate 10 samples of the ContinuousUniform distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(continuousUniform.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the ContinuousUniform(0, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the ContinuousUniform(0, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = continuousUniform.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the ContinuousUniform(2, 10) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the ContinuousUniform(2, 10) distribution and display histogram");
+            continuousUniform.Upper = 10;
+            continuousUniform.Lower = 2;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = continuousUniform.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/ErlangDistribution.cs

@@ -0,0 +1,152 @@
+// <copyright file="ErlangDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Erlang distribution example
+    /// </summary>
+    public class ErlangDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/ErlangDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Erlang distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Erlang distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Erlang_distribution">Erlang distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Erlang distribution class with parameters Shape = 1, Scale = 2.
+            var erlang = new Erlang(1, 2.0);
+            Console.WriteLine(@"1. Initialize the new instance of the Erlang distribution class with parameters Shape = {0}, Scale = {1}", erlang.Shape, erlang.Scale);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", erlang);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", erlang.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", erlang.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", erlang.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", erlang.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", erlang.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", erlang.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", erlang.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", erlang.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", erlang.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", erlang.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", erlang.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Erlang distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Erlang distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(erlang.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Erlang(1, 2.0) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Erlang(1, 2.0) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = erlang.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Erlang(3, 2.0) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Erlang(3, 2.0) distribution and display histogram");
+            erlang.Shape = 3;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = erlang.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Erlang(9, 0.5) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Erlang(9, 0.5) distribution and display histogram");
+            erlang.Shape = 9;
+            erlang.Scale = 0.5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = erlang.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/ExponentialDistribution.cs

@@ -0,0 +1,154 @@
+// <copyright file="ExponentialDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Exponential distribution example
+    /// </summary>
+    public class ExponentialDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/ExponentialDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Exponential distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Exponential distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Exponential_distribution">Exponential distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Exponential distribution class with parameter Lambda = 1.
+            var exponential = new Exponential(1);
+            Console.WriteLine(@"1. Initialize the new instance of the Exponential distribution class with parameter Lambda = {0}", exponential.Lambda);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", exponential);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", exponential.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", exponential.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", exponential.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", exponential.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", exponential.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", exponential.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", exponential.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", exponential.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", exponential.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", exponential.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", exponential.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", exponential.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Exponential distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Exponential distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(exponential.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Exponential(1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Exponential(1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = exponential.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Exponential(9) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Exponential(9) distribution and display histogram");
+            exponential.Lambda = 9;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = exponential.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram");
+            exponential.Lambda = 0.01;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = exponential.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/FisherSnedecorDistribution.cs

@@ -0,0 +1,150 @@
+// <copyright file="FisherSnedecorDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// FisherSnedecor distribution example
+    /// </summary>
+    public class FisherSnedecorDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/FisherZDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "FisherSnedecor distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "FisherSnedecor distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/F-distribution">FisherSnedecor distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the FisherSnedecor distribution class with parameter DegreeOfFreedom1 = 50, DegreeOfFreedom2 = 20.
+            var fisherSnedecor = new FisherSnedecor(50, 20);
+            Console.WriteLine(@"1. Initialize the new instance of the FisherSnedecor distribution class with parameters DegreeOfFreedom1 = {0}, DegreeOfFreedom2 = {1}", fisherSnedecor.DegreeOfFreedom1, fisherSnedecor.DegreeOfFreedom2);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", fisherSnedecor);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", fisherSnedecor.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", fisherSnedecor.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", fisherSnedecor.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", fisherSnedecor.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", fisherSnedecor.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", fisherSnedecor.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", fisherSnedecor.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", fisherSnedecor.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", fisherSnedecor.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", fisherSnedecor.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the FisherSnedecor distribution
+            Console.WriteLine(@"3. Generate 10 samples of the FisherSnedecor distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(fisherSnedecor.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the FisherSnedecor(50, 20) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = fisherSnedecor.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the FisherSnedecor(20, 10) distribution and display histogram");
+            fisherSnedecor.DegreeOfFreedom1 = 20;
+            fisherSnedecor.DegreeOfFreedom2 = 10;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = fisherSnedecor.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the FisherSnedecor(100, 100) distribution and display histogram");
+            fisherSnedecor.DegreeOfFreedom1 = 100;
+            fisherSnedecor.DegreeOfFreedom2 = 100;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = fisherSnedecor.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/GammaDistribution.cs

@@ -0,0 +1,141 @@
+// <copyright file="GammaDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Gamma distribution example
+    /// </summary>
+    public class GammaDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/GammaDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Gamma distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Gamma distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Gamma_distribution">Gamma distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Gamma distribution class with parameter Shape = 1, Scale = 0.5.
+            var gamma = new Gamma(1, 2.0);
+            Console.WriteLine(@"1. Initialize the new instance of the Gamma distribution class with parameters Shape = {0}, Scale = {1}", gamma.Shape, gamma.Scale);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", gamma);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", gamma.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", gamma.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", gamma.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", gamma.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", gamma.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", gamma.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", gamma.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", gamma.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", gamma.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", gamma.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", gamma.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Gamma distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Gamma distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(gamma.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Gamma(1, 2) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Gamma(1, 2) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = gamma.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Gamma(8) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Gamma(5, 1) distribution and display histogram");
+            gamma.Shape = 5;
+            gamma.Scale = 1;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = gamma.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/InverseGammaDistribution.cs

@@ -0,0 +1,151 @@
+// <copyright file="InverseGammaDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// InverseGamma distribution example
+    /// </summary>
+    public class InverseGammaDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/InverseGammaDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "InverseGamma distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "InverseGamma distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Inverse-gamma_distribution">InverseGamma distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the InverseGamma distribution class with parameters shape = 4, scale = 0.5
+            var inverseGamma = new InverseGamma(4, 0.5);
+            Console.WriteLine(@"1. Initialize the new instance of the InverseGamma distribution class with parameters Shape = {0}, Scale = {1}", inverseGamma.Shape, inverseGamma.Scale);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", inverseGamma);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", inverseGamma.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", inverseGamma.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", inverseGamma.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", inverseGamma.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", inverseGamma.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", inverseGamma.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", inverseGamma.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", inverseGamma.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", inverseGamma.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", inverseGamma.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", inverseGamma.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the InverseGamma distribution
+            Console.WriteLine(@"3. Generate 10 samples of the InverseGamma distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(inverseGamma.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the InverseGamma(4, 0.5) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = inverseGamma.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the InverseGamma(8, 0.5) distribution and display histogram");
+            inverseGamma.Shape = 8;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = inverseGamma.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the InverseGamma(2, 1) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the InverseGamma(8, 2) distribution and display histogram");
+            inverseGamma.Scale = 2;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = inverseGamma.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/LaplaceDistribution.cs

@@ -0,0 +1,155 @@
+// <copyright file="LaplaceDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Laplace distribution example
+    /// </summary>
+    public class LaplaceDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/LaplaceDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Laplace distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Laplace distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Laplace_distribution">Laplace distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Laplace distribution class with parameters Location = {0}, Scale = {1}
+            var laplace = new Laplace(0, 1);
+            Console.WriteLine(@"1. Initialize the new instance of the Laplace distribution class with parameters Location = {0}, Scale = {1}", laplace.Location, laplace.Scale);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", laplace);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", laplace.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", laplace.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", laplace.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", laplace.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", laplace.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", laplace.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", laplace.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", laplace.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", laplace.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", laplace.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", laplace.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", laplace.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Laplace distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Laplace distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(laplace.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Laplace(0, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Laplace(0, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = laplace.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Laplace(0, 4) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Laplace(0, 4) distribution and display histogram");
+            data = new double[100000];
+            laplace.Scale = 4;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = laplace.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Laplace(-10, 4) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Laplace(-10 4) distribution and display histogram");
+            laplace.Location = -10;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = laplace.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/LogNormalDistribution.cs

@@ -0,0 +1,155 @@
+// <copyright file="LogNormalDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// LogNormal distribution example
+    /// </summary>
+    public class LogNormalDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/LogNormalDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "LogNormal distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "LogNormal distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Log-normal_distribution">LogNormal distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the LogNormal distribution class with parameters Mu = 0, Sigma = 1
+            var logNormal = new LogNormal(0, 1);
+            Console.WriteLine(@"1. Initialize the new instance of the LogNormal distribution class with parameters Mu = {0}, Sigma = {1}", logNormal.Mu, logNormal.Sigma);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", logNormal);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", logNormal.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", logNormal.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", logNormal.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", logNormal.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", logNormal.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", logNormal.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", logNormal.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", logNormal.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", logNormal.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", logNormal.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", logNormal.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", logNormal.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples
+            Console.WriteLine(@"3. Generate 10 samples");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(logNormal.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the LogNormal(0, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the LogNormal(0, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = logNormal.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the LogNormal(0, 0.5) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the LogNormal(0, 0.5) distribution and display histogram");
+            logNormal.Sigma = 0.5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = logNormal.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the LogNormal(5, 0.25) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the LogNormal(5, 0.25) distribution and display histogram");
+            logNormal.Mu = 5;
+            logNormal.Sigma = 0.25;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = logNormal.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/NormalDistribution.cs

@@ -0,0 +1,144 @@
+// <copyright file="NormalDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Normal distribution example
+    /// </summary>
+    public class NormalDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/NormalDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Normal distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Normal distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Normal_distribution">Normal distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Normal distribution class with parameters Mean = 0, StdDev = 1
+            var normal = new Normal(0, 1);
+            Console.WriteLine(@"1. Initialize the new instance of the Normal distribution class with parameters Mean = {0}, StdDev = {1}", normal.Mean, normal.StdDev);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", normal);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", normal.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", normal.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", normal.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", normal.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", normal.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", normal.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", normal.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", normal.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", normal.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", normal.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", normal.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", normal.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples
+            Console.WriteLine(@"3. Generate 10 samples");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(normal.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Normal(0, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Normal(0, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = normal.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Normal(-10, 0.2) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Normal(-10, 0.01) distribution and display histogram");
+            normal.Mean = -10;
+            normal.StdDev = 0.01;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = normal.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/ParetoDistribution.cs

@@ -0,0 +1,155 @@
+// <copyright file="ParetoDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Pareto distribution example
+    /// </summary>
+    public class ParetoDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/ParetoDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Pareto distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Pareto distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Pareto_distribution">Pareto distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Pareto distribution class with parameters Shape = 3, Scale = 1
+            var pareto = new Pareto(1, 3);
+            Console.WriteLine(@"1. Initialize the new instance of the Pareto distribution class with parameters Shape = {0}, Scale = {1}", pareto.Shape, pareto.Scale);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", pareto);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", pareto.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", pareto.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", pareto.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", pareto.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", pareto.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", pareto.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", pareto.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", pareto.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", pareto.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", pareto.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", pareto.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", pareto.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Pareto distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Pareto distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(pareto.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Pareto(1, 3) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = pareto.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Pareto(1, 1) distribution and display histogram");
+            pareto.Shape = 1;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = pareto.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Pareto(10, 5) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Pareto(10, 50) distribution and display histogram");
+            pareto.Shape = 50;
+            pareto.Scale = 10;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = pareto.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/RayleighDistribution.cs

@@ -0,0 +1,154 @@
+// <copyright file="RayleighDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Rayleigh distribution example
+    /// </summary>
+    public class RayleighDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/RayleighDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Rayleigh distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Rayleigh distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Rayleigh_distribution">Rayleigh distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Rayleigh distribution class with parameter Scale = 1.
+            var rayleigh = new Rayleigh(1);
+            Console.WriteLine(@"1. Initialize the new instance of the Rayleigh distribution class with parameter Scale = {0}", rayleigh.Scale);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", rayleigh);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", rayleigh.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", rayleigh.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", rayleigh.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", rayleigh.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", rayleigh.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", rayleigh.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", rayleigh.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", rayleigh.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", rayleigh.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", rayleigh.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", rayleigh.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", rayleigh.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Rayleigh distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Rayleigh distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(rayleigh.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Rayleigh(1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Rayleigh(1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = rayleigh.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Rayleigh(4) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Rayleigh(4) distribution and display histogram");
+            rayleigh.Scale = 4;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = rayleigh.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Rayleigh(0.5) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Rayleigh(0.5) distribution and display histogram");
+            rayleigh.Scale = 0.5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = rayleigh.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/StableDistribution.cs

@@ -0,0 +1,154 @@
+// <copyright file="StableDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Stable distribution example
+    /// </summary>
+    public class StableDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/StableDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Stable distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Stable distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Stable_distribution">Stable distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Stable distribution class with parameters Alpha = 2.0, Beta = 0, Scale = 1, Location = 0.
+            var stable = new Stable(2.0, 0, 1, 0);
+            Console.WriteLine(@"1. Initialize the new instance of the Stable distribution class with parameters Alpha = {0}, Beta = {1}, Scale = {2}, Location = {3}", stable.Alpha, stable.Beta, stable.Scale, stable.Location);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", stable);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", stable.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", stable.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", stable.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", stable.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", stable.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", stable.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", stable.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", stable.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", stable.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", stable.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", stable.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Stable distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Stable distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(stable.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Stable(1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Stable(2, 0, 1, 0) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = stable.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Stable(1, 0, 1, 0) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Stable(1, 0, 1, 0) distribution and display histogram");
+            stable.Alpha = 1;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = stable.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Stable(1.5, 1, 1, 5) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Stable(1.5, 1, 1, 5) distribution and display histogram");
+            stable.Alpha = 1.5;
+            stable.Beta = 1;
+            stable.Location = 5;
+            stable.Scale = 5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = stable.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/StudentTDistribution.cs

@@ -0,0 +1,149 @@
+// <copyright file="StudentTDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// StudentT distribution example
+    /// </summary>
+    public class StudentTDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/StudentTDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "StudentT distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "StudentT distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/StudentT_distribution">StudentT distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the StudentT distribution class with parameters Location = 0, Scale = 1, DegreesOfFreedom = 1
+            var studentT = new StudentT();
+            Console.WriteLine(@"1. Initialize the new instance of the StudentT distribution class with parameters Location = {0}, Scale = {1}, DegreesOfFreedom = {2}", studentT.Location, studentT.Scale, studentT.DegreesOfFreedom);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", studentT);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", studentT.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", studentT.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", studentT.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", studentT.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", studentT.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", studentT.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", studentT.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", studentT.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", studentT.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", studentT.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", studentT.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // 3. Generate 10 samples of the StudentT distribution
+            Console.WriteLine(@"3. Generate 10 samples of the StudentT distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(studentT.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the StudentT(0, 1, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the StudentT(0, 1, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = studentT.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+
+            // 5. Generate 100000 samples of the StudentT(0, 1, 5) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the StudentT(0, 1, 5) distribution and display histogram");
+            studentT.DegreesOfFreedom = 5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = studentT.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the StudentT(0, 1, 10) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the StudentT(0, 1, 10) distribution and display histogram");
+            studentT.DegreesOfFreedom = 10;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = studentT.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/ContinuousDistributions/WeibullDistribution.cs

@@ -0,0 +1,154 @@
+// <copyright file="WeibullDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.ContinuousDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Weibull distribution example
+    /// </summary>
+    public class WeibullDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/WeibullDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Weibull distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Weibull distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Weibull_distribution">Weibull distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Weibull distribution class with parameters Scale = 1, Shape = 0.5
+            var weibull = new Weibull(0.5, 1);
+            Console.WriteLine(@"1. Initialize the new instance of the Weibull distribution class with parameterы Scale = {0}, Shape = {1}", weibull.Scale, weibull.Shape);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", weibull);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", weibull.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability density at location '0.3'", weibull.Density(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability density at location '0.3'", weibull.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", weibull.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", weibull.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", weibull.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", weibull.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Median
+            Console.WriteLine(@"{0} - Median", weibull.Median.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", weibull.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", weibull.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", weibull.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", weibull.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Weibull distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Weibull distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(weibull.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Weibull(0.5, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Weibull(0.5, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = weibull.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Weibull(1.5, 1) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Weibull(1.5, 1) distribution and display histogram");
+            weibull.Shape = 1.5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = weibull.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Weibull(5, 1) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Weibull(5, 1) distribution and display histogram");
+            weibull.Shape = 5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = weibull.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/BernoulliDistribution.cs

@@ -0,0 +1,151 @@
+// <copyright file="BernoulliDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Bernoulli distribution example
+    /// </summary>
+    public class BernoulliDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/BernoulliDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Bernoulli distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Bernoulli distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Bernoulli_distribution">Bernoulli distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Bernoulli distribution class with parameter P = 0.2
+            var bernoulli = new Bernoulli(0.2);
+            Console.WriteLine(@"1. Initialize the new instance of the Bernoulli distribution class with parameter P = {0}", bernoulli.P);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", bernoulli);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", bernoulli.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", bernoulli.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", bernoulli.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", bernoulli.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", bernoulli.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", bernoulli.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", bernoulli.Mean.ToString(" #0.00000;-#0.00000"));
+
+            // Mode
+            Console.WriteLine(@"{0} - Mode", bernoulli.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", bernoulli.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", bernoulli.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", bernoulli.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Bernoulli distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Bernoulli distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(bernoulli.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Bernoulli(0.2) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Bernoulli(0.2) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = bernoulli.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Bernoulli(4) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Bernoulli(0.9) distribution and display histogram");
+            bernoulli.P = 0.9;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = bernoulli.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Bernoulli(8) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Bernoulli(0.5) distribution and display histogram");
+            bernoulli.P = 0.5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = bernoulli.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/BinomialDistribution.cs

@@ -0,0 +1,155 @@
+// <copyright file="BinomialDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Binomial distribution example
+    /// </summary>
+    public class BinomialDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/BinomialDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Binomial distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Binomial distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Binomial_distribution">Binomial distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Binomial distribution class with parameters P = 0.2, N = 20
+            var binomial = new Binomial(0.2, 20);
+            Console.WriteLine(@"1. Initialize the new instance of the Binomial distribution class with parameters P = {0}, N = {1}", binomial.P, binomial.N);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", binomial);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", binomial.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", binomial.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Median
+            Console.WriteLine(@"{0} - Median", binomial.Median.ToString(" #0.00000;-#0.00000"));
+            
+            // Mode
+            Console.WriteLine(@"{0} - Mode", binomial.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", binomial.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Binomial distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Binomial distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(binomial.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Binomial(0.2, 20) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Binomial(0.2, 20) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Binomial(0.7, 20) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Binomial(0.7, 20) distribution and display histogram");
+            binomial.P = 0.7;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Binomial(0.5, 40) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Binomial(0.5, 40) distribution and display histogram");
+            binomial.P = 0.5;
+            binomial.N = 40;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/CategoricalDistribution.cs

@@ -0,0 +1,135 @@
+// <copyright file="CategoricalDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Categorical distribution example
+    /// </summary>
+    public class CategoricalDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Categorical distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Categorical distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Categorical_distribution">Categorical distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)
+            var binomial = new Categorical(new[] { 0.1, 0.2, 0.25, 0.45 });
+            Console.WriteLine(@"1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)");
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", binomial);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", binomial.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", binomial.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Median
+            Console.WriteLine(@"{0} - Median", binomial.Median.ToString(" #0.00000;-#0.00000"));
+            
+            // Variance
+            Console.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // 3. Generate 10 samples of the Categorical distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Categorical distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(binomial.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Categorical(new []{ 0.1, 0.2, 0.25, 0.45 }) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Categorical(0.2, 20) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Categorical(new []{ 0.6, 0.2, 0.1, 0.1 }) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Categorical(0.7, 20) distribution and display histogram");
+            binomial.P = new[] { 0.6, 0.2, 0.1, 0.1 };
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/ConwayMaxwellPoissonDistribution.cs

@@ -0,0 +1,139 @@
+// <copyright file="ConwayMaxwellPoissonDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// ConwayMaxwellPoisson distribution example
+    /// </summary>
+    public class ConwayMaxwellPoissonDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "ConwayMaxwellPoisson distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "ConwayMaxwellPoisson distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution">ConwayMaxwellPoisson distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = 2, Nu = 1
+            var binomial = new ConwayMaxwellPoisson(2, 1);
+            Console.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", binomial.Lambda, binomial.Nu);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", binomial);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Variance
+            Console.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the ConwayMaxwellPoisson distribution
+            Console.WriteLine(@"3. Generate 10 samples of the ConwayMaxwellPoisson distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(binomial.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the ConwayMaxwellPoisson(4, 1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the ConwayMaxwellPoisson(2, 1) distribution and display histogram");
+            binomial.Lambda = 2;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the ConwayMaxwellPoisson(5, 2) distribution and display histogram");
+            binomial.Lambda = 5;
+            binomial.Nu = 2;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = binomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/DiscreteUniformDistribution.cs

@@ -0,0 +1,156 @@
+// <copyright file="DiscreteUniformDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// DiscreteUniform distribution example
+    /// </summary>
+    public class DiscreteUniformDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/DiscreteUniformDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "DiscreteUniform distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "DiscreteUniform distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Discrete_uniform">DiscreteUniform distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = 2, UpperBound = 10
+            var discreteUniform = new DiscreteUniform(2, 10);
+            Console.WriteLine(@"1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = {0}, UpperBound = {1}", discreteUniform.LowerBound, discreteUniform.UpperBound);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", discreteUniform);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", discreteUniform.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", discreteUniform.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", discreteUniform.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", discreteUniform.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", discreteUniform.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", discreteUniform.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", discreteUniform.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Median
+            Console.WriteLine(@"{0} - Median", discreteUniform.Median.ToString(" #0.00000;-#0.00000"));
+            
+            // Mode
+            Console.WriteLine(@"{0} - Mode", discreteUniform.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", discreteUniform.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", discreteUniform.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", discreteUniform.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the DiscreteUniform distribution
+            Console.WriteLine(@"3. Generate 10 samples of the DiscreteUniform distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(discreteUniform.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the DiscreteUniform(2, 10) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the DiscreteUniform(2, 10) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = discreteUniform.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the DiscreteUniform(-10, 10) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the DiscreteUniform(-10, 10) distribution and display histogram");
+            discreteUniform.LowerBound = -10;
+            discreteUniform.UpperBound = 10;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = discreteUniform.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the DiscreteUniform(0, 40) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the DiscreteUniform(0, 40) distribution and display histogram");
+            discreteUniform.LowerBound = 0;
+            discreteUniform.UpperBound = 40;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = discreteUniform.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/GeometricDistribution.cs

@@ -0,0 +1,154 @@
+// <copyright file="GeometricDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Geometric distribution example
+    /// </summary>
+    public class GeometricDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/GeometricDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Geometric distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Geometric distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Geometric_distribution">Geometric distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Geometric distribution class with parameter P = 0.2
+            var geometric = new Geometric(0.2);
+            Console.WriteLine(@"1. Initialize the new instance of the Geometric distribution class with parameter P = {0}", geometric.P);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", geometric);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", geometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", geometric.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", geometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", geometric.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", geometric.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", geometric.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", geometric.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Median
+            Console.WriteLine(@"{0} - Median", geometric.Median.ToString(" #0.00000;-#0.00000"));
+            
+            // Mode
+            Console.WriteLine(@"{0} - Mode", geometric.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", geometric.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", geometric.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", geometric.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Geometric distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Geometric distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(geometric.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Geometric(0.2, 20) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Geometric(0.2, 20) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = geometric.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Geometric(0.5) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Geometric(0.5) distribution and display histogram");
+            geometric.P = 0.5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = geometric.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Geometric(0.8) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Geometric(0.8) distribution and display histogram");
+            geometric.P = 0.8;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = geometric.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/HypergeometricDistribution.cs

@@ -0,0 +1,139 @@
+// <copyright file="HypergeometricDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Hypergeometric distribution example
+    /// </summary>
+    public class HypergeometricDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/HypergeometricDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Hypergeometric distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Hypergeometric distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Hypergeometric_distribution">Hypergeometric distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Hypergeometric distribution class with parameters PopulationSize = 10, M = 2, N = 8
+            var hypergeometric = new Hypergeometric(30, 15, 10);
+            Console.WriteLine(@"1. Initialize the new instance of the Hypergeometric distribution class with parameters PopulationSize = {0}, M = {1}, N = {2}", hypergeometric.PopulationSize, hypergeometric.M, hypergeometric.N);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", hypergeometric);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", hypergeometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", hypergeometric.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", hypergeometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", hypergeometric.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", hypergeometric.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", hypergeometric.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Mode
+            Console.WriteLine(@"{0} - Mode", hypergeometric.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", hypergeometric.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", hypergeometric.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", hypergeometric.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Hypergeometric distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Hypergeometric distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(hypergeometric.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Hypergeometric(30, 15, 10) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Hypergeometric(30, 15, 10) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = hypergeometric.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Hypergeometric(52, 13, 5) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Hypergeometric(52, 13, 5) distribution and display histogram");
+            hypergeometric.PopulationSize = 52;
+            hypergeometric.M = 13;
+            hypergeometric.N = 5;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = hypergeometric.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/NegativeBinomialDistribution.cs

@@ -0,0 +1,149 @@
+// <copyright file="NegativeBinomialDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// NegativeBinomial distribution example
+    /// </summary>
+    public class NegativeBinomialDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/NegativeBinomialDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "NegativeBinomial distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "NegativeBinomial distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Negative_binomial">NegativeBinomial distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = 0.2, R = 20
+            var negativeBinomial = new NegativeBinomial(20, 0.2);
+            Console.WriteLine(@"1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = {0}, N = {1}", negativeBinomial.P, negativeBinomial.R);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", negativeBinomial);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", negativeBinomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", negativeBinomial.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", negativeBinomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", negativeBinomial.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", negativeBinomial.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", negativeBinomial.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Mode
+            Console.WriteLine(@"{0} - Mode", negativeBinomial.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", negativeBinomial.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", negativeBinomial.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", negativeBinomial.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the NegativeBinomial distribution
+            Console.WriteLine(@"3. Generate 10 samples of the NegativeBinomial distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(negativeBinomial.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the NegativeBinomial(0.2, 20) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the NegativeBinomial(0.2, 20) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = negativeBinomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the NegativeBinomial(0.7, 20) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the NegativeBinomial(0.7, 20) distribution and display histogram");
+            negativeBinomial.P = 0.7;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = negativeBinomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the NegativeBinomial(0.5, 1) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the NegativeBinomial(0.5, 1) distribution and display histogram");
+            negativeBinomial.P = 0.5;
+            negativeBinomial.R = 1;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = negativeBinomial.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/PoissonDistribution.cs

@@ -0,0 +1,154 @@
+// <copyright file="PoissonDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Poisson distribution example
+    /// </summary>
+    public class PoissonDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/PoissonDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Poisson distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Poisson distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Poisson distribution class with parameter Lambda = 1
+            var poisson = new Poisson(1);
+            Console.WriteLine(@"1. Initialize the new instance of the Poisson distribution class with parameter Lambda = {0}", poisson.Lambda);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", poisson);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", poisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", poisson.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", poisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", poisson.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", poisson.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", poisson.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", poisson.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Median
+            Console.WriteLine(@"{0} - Median", poisson.Median.ToString(" #0.00000;-#0.00000"));
+            
+            // Mode
+            Console.WriteLine(@"{0} - Mode", poisson.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", poisson.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", poisson.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", poisson.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Poisson distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Poisson distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(poisson.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Poisson(1) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Poisson(1) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = poisson.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Poisson(4) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Poisson(4) distribution and display histogram");
+            poisson.Lambda = 4;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = poisson.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Poisson(10) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Poisson(10) distribution and display histogram");
+            poisson.Lambda = 10;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = poisson.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/DiscreteDistributions/ZipfDistribution.cs

@@ -0,0 +1,152 @@
+// <copyright file="ZipfDistribution.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.DiscreteDistributions
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+
+    /// <summary>
+    /// Zipf distribution example
+    /// </summary>
+    public class ZipfDistribution : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        /// <seealso cref="http://reference.wolfram.com/mathematica/ref/ZipfDistribution.html"/>
+        public string Name
+        {
+            get
+            {
+                return "Zipf distribution";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Zipf distribution properties and samples generating examples";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <a href="http://en.wikipedia.org/wiki/Zipf_distribution">Zipf distribution</a>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the Zipf distribution class with parameters S = 5, N = 10
+            var zipf = new Zipf(5, 10);
+            Console.WriteLine(@"1. Initialize the new instance of the Zipf distribution class with parameters S = {0}, N = {1}", zipf.S, zipf.N);
+            Console.WriteLine();
+
+            // 2. Distributuion properties:
+            Console.WriteLine(@"2. {0} distributuion properties:", zipf);
+
+            // Cumulative distribution function
+            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", zipf.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
+
+            // Probability density
+            Console.WriteLine(@"{0} - Probability mass at location '3'", zipf.Probability(3).ToString(" #0.00000;-#0.00000"));
+
+            // Log probability density
+            Console.WriteLine(@"{0} - Log probability mass at location '3'", zipf.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
+
+            // Entropy
+            Console.WriteLine(@"{0} - Entropy", zipf.Entropy.ToString(" #0.00000;-#0.00000"));
+
+            // Largest element in the domain
+            Console.WriteLine(@"{0} - Largest element in the domain", zipf.Maximum.ToString(" #0.00000;-#0.00000"));
+
+            // Smallest element in the domain
+            Console.WriteLine(@"{0} - Smallest element in the domain", zipf.Minimum.ToString(" #0.00000;-#0.00000"));
+
+            // Mean
+            Console.WriteLine(@"{0} - Mean", zipf.Mean.ToString(" #0.00000;-#0.00000"));
+            
+            // Mode
+            Console.WriteLine(@"{0} - Mode", zipf.Mode.ToString(" #0.00000;-#0.00000"));
+
+            // Variance
+            Console.WriteLine(@"{0} - Variance", zipf.Variance.ToString(" #0.00000;-#0.00000"));
+
+            // Standard deviation
+            Console.WriteLine(@"{0} - Standard deviation", zipf.StdDev.ToString(" #0.00000;-#0.00000"));
+
+            // Skewness
+            Console.WriteLine(@"{0} - Skewness", zipf.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 3. Generate 10 samples of the Zipf distribution
+            Console.WriteLine(@"3. Generate 10 samples of the Zipf distribution");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(zipf.Sample().ToString("N05") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Generate 100000 samples of the Zipf(5, 10) distribution and display histogram
+            Console.WriteLine(@"4. Generate 100000 samples of the Zipf(5, 10) distribution and display histogram");
+            var data = new double[100000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = zipf.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 5. Generate 100000 samples of the Zipf(2, 10) distribution and display histogram
+            Console.WriteLine(@"5. Generate 100000 samples of the Zipf(2, 10) distribution and display histogram");
+            zipf.S = 2;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = zipf.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+            Console.WriteLine();
+
+            // 6. Generate 100000 samples of the Zipf(5, 20) distribution and display histogram
+            Console.WriteLine(@"6. Generate 100000 samples of the Zipf(1, 20) distribution and display histogram");
+            zipf.S = 1;
+            zipf.N = 20;
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = zipf.Sample();
+            }
+
+            ConsoleHelper.DisplayHistogram(data);
+        }
+    }
+}

src/Examples/Examples.csproj

@@ -68,11 +68,51 @@
     <Reference Include="System.Xml" />
   </ItemGroup>
   <ItemGroup>
+    <Compile Include="ConsoleHelper.cs" />
+    <Compile Include="ContinuousDistributions\CauchyDistribution.cs" />
+    <Compile Include="ContinuousDistributions\ChiDistribution.cs" />
+    <Compile Include="ContinuousDistributions\ChiSquareDistribution.cs" />
+    <Compile Include="ContinuousDistributions\ContinuousUniformDistribution.cs" />
+    <Compile Include="ContinuousDistributions\WeibullDistribution.cs" />
+    <Compile Include="ContinuousDistributions\StudentTDistribution.cs" />
+    <Compile Include="ContinuousDistributions\StableDistribution.cs" />
+    <Compile Include="ContinuousDistributions\RayleighDistribution.cs" />
+    <Compile Include="ContinuousDistributions\ParetoDistribution.cs" />
+    <Compile Include="ContinuousDistributions\NormalDistribution.cs" />
+    <Compile Include="ContinuousDistributions\LogNormalDistribution.cs" />
+    <Compile Include="ContinuousDistributions\LaplaceDistribution.cs" />
+    <Compile Include="ContinuousDistributions\InverseGammaDistribution.cs" />
+    <Compile Include="ContinuousDistributions\GammaDistribution.cs" />
+    <Compile Include="ContinuousDistributions\FisherSnedecorDistribution.cs" />
+    <Compile Include="ContinuousDistributions\ExponentialDistribution.cs" />
+    <Compile Include="ContinuousDistributions\ErlangDistribution.cs" />
+    <Compile Include="Statistics.cs" />
+    <Compile Include="NumberTheory.cs" />
+    <Compile Include="Interpolation\AkimaSpline.cs" />
+    <Compile Include="Interpolation\RationalWithPoles.cs" />
+    <Compile Include="Interpolation\LinearBetweenPoints.cs" />
+    <Compile Include="Interpolation\RationalWithoutPoles.cs" />
+    <Compile Include="DiscreteDistributions\BernoulliDistribution.cs" />
+    <Compile Include="DiscreteDistributions\BinomialDistribution.cs" />
+    <Compile Include="DiscreteDistributions\CategoricalDistribution.cs" />
+    <Compile Include="DiscreteDistributions\ConwayMaxwellPoissonDistribution.cs" />
+    <Compile Include="DiscreteDistributions\ZipfDistribution.cs" />
+    <Compile Include="DiscreteDistributions\PoissonDistribution.cs" />
+    <Compile Include="DiscreteDistributions\NegativeBinomialDistribution.cs" />
+    <Compile Include="DiscreteDistributions\HypergeometricDistribution.cs" />
+    <Compile Include="DiscreteDistributions\GeometricDistribution.cs" />
+    <Compile Include="DiscreteDistributions\DiscreteUniformDistribution.cs" />
     <Compile Include="IExample.cs" />
+    <Compile Include="ContinuousDistributions\BetaDistribution.cs" />
     <Compile Include="LinearAlgebra\DirectSolvers.cs" />
     <Compile Include="LinearAlgebra\Factorization\Cholesky.cs" />
     <Compile Include="LinearAlgebra\Factorization\Evd.cs" />
     <Compile Include="LinearAlgebra\Factorization\LU.cs" />
+    <Compile Include="LinearAlgebra\IterativeSolvers\BiCgStabSolver.cs" />
+    <Compile Include="LinearAlgebra\IterativeSolvers\CompositeSolverExample.cs" />
+    <Compile Include="LinearAlgebra\IterativeSolvers\GpBiCgSolver.cs" />
+    <Compile Include="LinearAlgebra\IterativeSolvers\MlkBiCgStabSolver.cs" />
+    <Compile Include="LinearAlgebra\IterativeSolvers\TFQMRSolver.cs" />
     <Compile Include="LinearAlgebra\MatrixNorms.cs" />
     <Compile Include="LinearAlgebra\Factorization\QR.cs" />
     <Compile Include="LinearAlgebra\Factorization\Svd.cs" />
@@ -87,6 +127,17 @@
     <Compile Include="LinearAlgebra\VectorDataAccessor.cs" />
     <Compile Include="LinearAlgebra\VectorInitialization.cs" />
     <Compile Include="Properties\AssemblyInfo.cs" />
+    <Compile Include="RandomNumberGeneration.cs" />
+    <Compile Include="Integration.cs" />
+    <Compile Include="Sampling\Chebyshev.cs" />
+    <Compile Include="Sampling\Random.cs" />
+    <Compile Include="Sampling\Equidistant.cs" />
+    <Compile Include="SpecialFunctions\Common.cs" />
+    <Compile Include="SpecialFunctions\Beta.cs" />
+    <Compile Include="SpecialFunctions\ErrorFunction.cs" />
+    <Compile Include="SpecialFunctions\Stability.cs" />
+    <Compile Include="SpecialFunctions\Factorial.cs" />
+    <Compile Include="SpecialFunctions\Gamma.cs" />
   </ItemGroup>
   <ItemGroup>
     <BootstrapperPackage Include="Microsoft.Net.Client.3.5">
@@ -111,6 +162,7 @@
       <Name>Numerics</Name>
     </ProjectReference>
   </ItemGroup>
+  <ItemGroup />
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
   <!-- To modify your build process, add your task inside one of the targets below and uncomment it. 
        Other similar extension points exist, see Microsoft.Common.targets.

src/Examples/Integration.cs

@@ -0,0 +1,105 @@
+// <copyright file="Integration.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples
+{
+    using System;
+    using MathNet.Numerics.Integration;
+
+    /// <summary>
+    /// Numeric Integration (Quadrature)
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Integrate.html"/>
+    public class Integration : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Numeric Integration";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Analytic integration of smooth functions with no discontinuitie or derivative discontinuities and no poles inside the interval";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Trapezoidal_rule">Trapezoidal rule</seealso>
+        public void Run()
+        {
+            // 1. Integrate x*x on interval [0, 10]
+            Console.WriteLine(@"1. Integrate x*x on interval [0, 10]");
+            var result = Integrate.OnClosedInterval(x => x * x, 0, 10);
+            Console.WriteLine(result);
+            Console.WriteLine();
+
+            // 2. Integrate 1/(x^3 + 1) on interval [0, 1]
+            Console.WriteLine(@"2. Integrate 1/(x^3 + 1) on interval [0, 1]");
+            result = Integrate.OnClosedInterval(x => 1 / (Math.Pow(x, 3) + 1), 0, 1);
+            Console.WriteLine(result);
+            Console.WriteLine();
+
+            // 3. Integrate f(x) = exp(-x/5) (2 + sin(2 * x)) on [0, 10]
+            Console.WriteLine(@"3. Integrate f(x) = exp(-x/5) (2 + sin(2 * x)) on [0, 10]");
+            result = Integrate.OnClosedInterval(x => Math.Exp(-x / 5) * (2 + Math.Sin(2 * x)), 0, 100);
+            Console.WriteLine(result);
+            Console.WriteLine();
+
+            // 4. Integrate target function with absolute error = 1E-4
+            Console.WriteLine(@"4. Integrate target function with absolute error = 1E-4 on [0, 10]");
+            Console.WriteLine(@"public static double TargetFunctionA(double x)
+{
+    return Math.Exp(-x / 5) * (2 + Math.Sin(2 * x));
+}");
+            result = Integrate.OnClosedInterval(TargetFunctionA, 0, 100, 1e-4);
+            Console.WriteLine(result);
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Test Function: f(x) = exp(-x/5) (2 + sin(2 * x))
+        /// </summary>
+        /// <param name="x">X parameter value</param>
+        /// <returns>Calculation result</returns>
+        public static double TargetFunctionA(double x)
+        {
+            return Math.Exp(-x / 5) * (2 + Math.Sin(2 * x));
+        }
+    }
+}

src/Examples/Interpolation/AkimaSpline.cs

@@ -0,0 +1,119 @@
+// <copyright file="AkimaSpline.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.Interpolation
+{
+    using System;
+    using MathNet.Numerics.Interpolation;
+    using MathNet.Numerics.Interpolation.Algorithms;
+    using MathNet.Numerics.Random;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Interpolation example
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Interpolation.html"/>
+    public class AkimaSpline : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Interpolation - Akima Spline";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Akima Spline Interpolation Algorithm";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Spline_interpolation">Spline interpolation</seealso>
+        public void Run()
+        {
+            // 1. Generate 10 samples of the function x*x-2*x on interval [0, 10]
+            Console.WriteLine(@"1. Generate 10 samples of the function x*x-2*x on interval [0, 10]");
+            double[] points;
+            var values = Sample.EquidistantInterval(TargetFunction, 0, 10, 10, out points);
+            Console.WriteLine();
+
+            // 2. Create akima spline interpolation 
+            var method = new AkimaSplineInterpolation(points, values);
+            Console.WriteLine(@"2. Create akima spline interpolation based on arbitrary points");
+            Console.WriteLine();
+
+            // 3. Check if interpolation support integration
+            Console.WriteLine(@"3. Support integration = {0}", ((IInterpolation)method).SupportsIntegration);
+            Console.WriteLine();
+
+            // 4. Check if interpolation support differentiation
+            Console.WriteLine(@"4. Support differentiation = {0}", ((IInterpolation)method).SupportsDifferentiation);
+            Console.WriteLine();
+
+            // 5. Differentiate at point 5.2
+            Console.WriteLine(@"5. Differentiate at point 5.2 = {0}", method.Differentiate(5.2));
+            Console.WriteLine();
+
+            // 6. Integrate at point 5.2
+            Console.WriteLine(@"6. Integrate at point 5.2 = {0}", method.Integrate(5.2));
+            Console.WriteLine();
+
+            // 7. Interpolate ten random points and compare to function results
+            Console.WriteLine(@"7. Interpolate ten random points and compare to function results");
+            var rng = new MersenneTwister(1);
+            for (var i = 0; i < 10; i++)
+            {
+                // Generate random value from [0, 10]
+                var point = rng.NextDouble() * 10;
+                Console.WriteLine(@"Interpolate at {0} = {1}. Function({0}) = {2}", point.ToString("N05"), method.Interpolate(point).ToString("N05"), TargetFunction(point).ToString("N05"));
+            }
+
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Test Function: f(x) = x * x - 2 * x
+        /// </summary>
+        /// <param name="x">X parameter value</param>
+        /// <returns>Calculation result</returns>
+        public static double TargetFunction(double x)
+        {
+            return (x * x) - (2 * x);
+        }
+    }
+}

src/Examples/Interpolation/LinearBetweenPoints.cs

@@ -0,0 +1,118 @@
+// <copyright file="LinearBetweenPoints.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.Interpolation
+{
+    using System;
+    using MathNet.Numerics.Interpolation;
+    using MathNet.Numerics.Random;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Interpolation example
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Interpolation.html"/>
+    public class LinearBetweenPoints : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Interpolation - Linear Between Points";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Linear Spline Interpolation Algorithm";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Spline_interpolation">Spline interpolation</seealso>
+        public void Run()
+        {
+            // 1. Generate 20 samples of the function x*x-2*x on interval [0, 10]
+            Console.WriteLine(@"1. Generate 20 samples of the function x*x-2*x on interval [0, 10]");
+            double[] points;
+            var values = Sample.EquidistantInterval(TargetFunction, 0, 10, 20, out points);
+            Console.WriteLine();
+
+            // 2. Create a linear spline interpolation based on arbitrary points 
+            var method = Interpolate.LinearBetweenPoints(points, values);
+            Console.WriteLine(@"2. Create a linear spline interpolation based on arbitrary points");
+            Console.WriteLine();
+
+            // 3. Check if interpolation support integration
+            Console.WriteLine(@"3. Support integration = {0}", method.SupportsIntegration);
+            Console.WriteLine();
+
+            // 4. Check if interpolation support differentiation
+            Console.WriteLine(@"4. Support differentiation = {0}", method.SupportsDifferentiation);
+            Console.WriteLine();
+
+            // 5. Differentiate at point 5.2
+            Console.WriteLine(@"5. Differentiate at point 5.2 = {0}", method.Differentiate(5.2));
+            Console.WriteLine();
+
+            // 6. Integrate at point 5.2
+            Console.WriteLine(@"6. Integrate at point 5.2 = {0}", method.Integrate(5.2));
+            Console.WriteLine();
+
+            // 7. Interpolate ten random points and compare to function results
+            Console.WriteLine(@"7. Interpolate ten random points and compare to function results");
+            var rng = new MersenneTwister(1);
+            for (var i = 0; i < 10; i++)
+            {
+                // Generate random value from [0, 10]
+                var point = rng.NextDouble() * 10;
+                Console.WriteLine(@"Interpolate at {0} = {1}. Function({0}) = {2}", point.ToString("N05"), method.Interpolate(point).ToString("N05"), TargetFunction(point).ToString("N05"));
+            }
+
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Test Function: f(x) = x * x - 2 * x
+        /// </summary>
+        /// <param name="x">X parameter value</param>
+        /// <returns>Calculation result</returns>
+        public static double TargetFunction(double x)
+        {
+            return (x * x) - (2 * x);
+        }
+    }
+}

src/Examples/Interpolation/RationalWithPoles.cs

@@ -0,0 +1,109 @@
+// <copyright file="RationalWithPoles.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.Interpolation
+{
+    using System;
+    using MathNet.Numerics.Interpolation;
+    using MathNet.Numerics.Random;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Interpolation example
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Interpolation.html"/>
+    public class RationalWithPoles : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Interpolation - Rational With Poles";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Rational Interpolation (with poles) using Roland Bulirsch and Josef Stoer's Algorithm";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // 1. Generate 20 samples of the function f(x) = x on interval [-5, 5]
+            Console.WriteLine(@"1. Generate 20 samples of the function f(x) = x on interval [-5, 5]");
+            double[] points;
+            var values = Sample.EquidistantInterval(TargetFunction, -5, 5, 20, out points);
+            Console.WriteLine();
+
+            // 2. Create a burlish stoer rational interpolation based on arbitrary points 
+            var method = Interpolate.RationalWithPoles(points, values);
+            Console.WriteLine(@"2. Create a burlish stoer rational interpolation based on arbitrary points");
+            Console.WriteLine();
+
+            // 3. Check if interpolation support integration
+            Console.WriteLine(@"3. Support integration = {0}", method.SupportsIntegration);
+            Console.WriteLine();
+
+            // 4. Check if interpolation support differentiation
+            Console.WriteLine(@"4. Support differentiation = {0}", method.SupportsDifferentiation);
+            Console.WriteLine();
+
+            // 5. Interpolate ten random points and compare to function results
+            Console.WriteLine(@"5. Interpolate ten random points and compare to function results");
+            var rng = new MersenneTwister(1);
+            for (var i = 0; i < 10; i++)
+            {
+                // Generate random value from [0, 5]
+                var point = rng.Next(0, 5);
+                Console.WriteLine(@"Interpolate at {0} = {1}. Function({0}) = {2}", point.ToString("N05"), method.Interpolate(point).ToString("N05"), TargetFunction(point).ToString("N05"));
+            }
+
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Test Function: f(x) = x * x + 10
+        /// </summary>
+        /// <param name="x">X parameter value</param>
+        /// <returns>Calculation result</returns>
+        public static double TargetFunction(double x)
+        {
+            return x;
+        }
+    }
+}

src/Examples/Interpolation/RationalWithoutPoles.cs

@@ -0,0 +1,111 @@
+// <copyright file="RationalWithoutPoles.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.Interpolation
+{
+    using System;
+    using MathNet.Numerics.Interpolation;
+    using MathNet.Numerics.Random;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Interpolation example
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Interpolation.html"/>
+    public class RationalWithoutPoles : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Interpolation - Rational Without Poles";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Barycentric Rational Interpolation without poles, using Mike Floater and Kai Hormann's Algorithm";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Interpolation">Interpolation</seealso>
+        public void Run()
+        {
+            // 1. Generate 10 samples of the function 1/(1+x*x) on interval [-5, 5]
+            Console.WriteLine(@"1. Generate 10 samples of the function 1/(1+x*x) on interval [-5, 5]");
+            double[] points;
+            var values = Sample.EquidistantInterval(TargetFunction, -5, 5, 10, out points);
+            Console.WriteLine();
+            
+            // 2. Create a floater hormann rational pole-free interpolation based on arbitrary points
+            // This method is used by default when create an interpolation using Interpolate.Common method
+            var method = Interpolate.RationalWithoutPoles(points, values);
+            Console.WriteLine(@"2. Create a floater hormann rational pole-free interpolation based on arbitrary points");
+            Console.WriteLine();
+
+            // 3. Check if interpolation support integration
+            Console.WriteLine(@"3. Support integration = {0}", method.SupportsIntegration);
+            Console.WriteLine();
+
+            // 4. Check if interpolation support differentiation
+            Console.WriteLine(@"4. Support differentiation = {0}", method.SupportsDifferentiation);
+            Console.WriteLine();
+
+            // 5. Interpolate ten random points and compare to function results
+            Console.WriteLine(@"5. Interpolate ten random points and compare to function results");
+            var rng = new MersenneTwister(1);
+            for (var i = 0; i < 10; i++)
+            {
+                // Generate random value from [0, 5]
+                var point = rng.NextDouble() * 5;
+                Console.WriteLine(@"Interpolate at {0} = {1}. Function({0}) = {2}", point.ToString("N05"), method.Interpolate(point).ToString("N05"), TargetFunction(point).ToString("N05"));
+            }
+
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Test Function: f(x) = 1 / (1 + (x * x))
+        /// </summary>
+        /// <param name="x">X parameter value</param>
+        /// <returns>Calculation result</returns>
+        public static double TargetFunction(double x)
+        {
+            return 1 / (1 + (x * x));
+        }
+    }
+}

src/Examples/LinearAlgebra/IterativeSolvers/BiCgStabSolver.cs

@@ -0,0 +1,141 @@
+// <copyright file="BiCgStabSolver.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.LinearAlgebra.IterativeSolvers
+{
+    using System;
+    using System.Globalization;
+    using MathNet.Numerics.LinearAlgebra.Double;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.StopCriterium;
+    using MathNet.Numerics.LinearAlgebra.Generic.Solvers.StopCriterium;
+
+    /// <summary>
+    /// BiCGStab Iterative solver
+    /// </summary>
+    /// <seealso cref="http://en.wikipedia.org/wiki/Biconjugate_gradient_stabilized_method"/>
+    public class BiCgStabSolver : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Bi-Conjugate Gradient Stabilized iterative solver";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Solve linear equation using Bi-Conjugate Gradient Stabilized (BiCGStab) solver";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Biconjugate_gradient_stabilized_method">Biconjugate gradient stabilized method</seealso>
+        public void Run()
+        {
+            // Format matrix output to console
+            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
+            formatProvider.TextInfo.ListSeparator = " ";
+
+            // Solve next system of linear equations (Ax=b):
+            // 5*x + 2*y - 4*z = -7
+            // 3*x - 7*y + 6*z = 38
+            // 4*x + 1*y + 5*z = 43
+
+            // Create matrix "A" with coefficients 
+            var matrixA = new DenseMatrix(new[,] { { 5.00, 2.00, -4.00 }, { 3.00, -7.00, 6.00 }, { 4.00, 1.00, 5.00 } });
+            Console.WriteLine(@"Matrix 'A' with coefficients");
+            Console.WriteLine(matrixA.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create vector "b" with the constant terms.
+            var vectorB = new DenseVector(new[] { -7.0, 38.0, 43.0 });
+            Console.WriteLine(@"Vector 'b' with the constant terms");
+            Console.WriteLine(vectorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create stop criteriums to monitor an iterative calculation. There are next available stop criteriums:
+            // - DivergenceStopCriterium: monitors an iterative calculation for signs of divergence;
+            // - FailureStopCriterium: monitors residuals for NaN's;
+            // - IterationCountStopCriterium: monitors the numbers of iteration steps;
+            // - ResidualStopCriterium: monitors residuals if calculation is considered converged;
+
+            // Stop calculation if 1000 iterations reached during calculation
+            var iterationCountStopCriterium = new IterationCountStopCriterium(1000);
+
+            // Stop calculation if residuals are below 1E-10 --> the calculation is considered converged
+            var residualStopCriterium = new ResidualStopCriterium(1e-10);
+ 
+            // Create monitor with defined stop criteriums
+            var monitor = new Iterator(new IIterationStopCriterium<double>[] { iterationCountStopCriterium, residualStopCriterium });
+
+            // Create Bi-Conjugate Gradient Stabilized solver
+            var solver = new BiCgStab(monitor);
+
+            // 1. Solve the matrix equation
+            var resultX = solver.Solve(matrixA, vectorB);
+            Console.WriteLine(@"1. Solve the matrix equation");
+            Console.WriteLine();
+
+            // 2. Check solver status of the iterations. 
+            // Solver has property IterationResult which contains the status of the iteration once the calculation is finished.
+            // Possible values are:
+            // - CalculationCancelled: calculation was cancelled by the user;
+            // - CalculationConverged: calculation has converged to the desired convergence levels;
+            // - CalculationDiverged: calculation diverged;
+            // - CalculationFailure: calculation has failed for some reason;
+            // - CalculationIndetermined: calculation is indetermined, not started or stopped;
+            // - CalculationRunning: calculation is running and no results are yet known;
+            // - CalculationStoppedWithoutConvergence: calculation has been stopped due to reaching the stopping limits, but that convergence was not achieved;
+            Console.WriteLine(@"2. Solver status of the iterations");
+            Console.WriteLine(solver.IterationResult);
+            Console.WriteLine();
+
+            // 3. Solution result vector of the matrix equation
+            Console.WriteLine(@"3. Solution result vector of the matrix equation");
+            Console.WriteLine(resultX.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // 4. Verify result. Multiply coefficient matrix "A" by result vector "x"
+            var reconstructVecorB = matrixA * resultX;
+            Console.WriteLine(@"4. Multiply coefficient matrix 'A' by result vector 'x'");
+            Console.WriteLine(reconstructVecorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/LinearAlgebra/IterativeSolvers/CompositeSolverExample.cs

@@ -0,0 +1,208 @@
+// <copyright file="CompositeSolverExample.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.LinearAlgebra.IterativeSolvers
+{
+    using System;
+    using System.Globalization;
+    using System.Reflection;
+    using MathNet.Numerics.LinearAlgebra.Double;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.StopCriterium;
+    using MathNet.Numerics.LinearAlgebra.Generic.Solvers;
+    using MathNet.Numerics.LinearAlgebra.Generic.Solvers.StopCriterium;
+
+    /// <summary>
+    /// Сomposite matrix solver
+    /// </summary>
+    public class CompositeSolverExample : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Composite matrix solver";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Solve linear equation using composite matrix solver. The actual solver is made by a sequence of matrix solvers";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // Format matrix output to console
+            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
+            formatProvider.TextInfo.ListSeparator = " ";
+
+            // Solve next system of linear equations (Ax=b):
+            // 5*x + 2*y - 4*z = -7
+            // 3*x - 7*y + 6*z = 38
+            // 4*x + 1*y + 5*z = 43
+
+            // Create matrix "A" with coefficients 
+            var matrixA = new DenseMatrix(new[,] { { 5.00, 2.00, -4.00 }, { 3.00, -7.00, 6.00 }, { 4.00, 1.00, 5.00 } });
+            Console.WriteLine(@"Matrix 'A' with coefficients");
+            Console.WriteLine(matrixA.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create vector "b" with the constant terms.
+            var vectorB = new DenseVector(new[] { -7.0, 38.0, 43.0 });
+            Console.WriteLine(@"Vector 'b' with the constant terms");
+            Console.WriteLine(vectorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create stop criteriums to monitor an iterative calculation. There are next available stop criteriums:
+            // - DivergenceStopCriterium: monitors an iterative calculation for signs of divergence;
+            // - FailureStopCriterium: monitors residuals for NaN's;
+            // - IterationCountStopCriterium: monitors the numbers of iteration steps;
+            // - ResidualStopCriterium: monitors residuals if calculation is considered converged;
+
+            // Stop calculation if 1000 iterations reached during calculation
+            var iterationCountStopCriterium = new IterationCountStopCriterium(1000);
+
+            // Stop calculation if residuals are below 1E-10 --> the calculation is considered converged
+            var residualStopCriterium = new ResidualStopCriterium(1e-10);
+
+            // Create monitor with defined stop criteriums
+            var monitor = new Iterator(new IIterationStopCriterium<double>[] { iterationCountStopCriterium, residualStopCriterium });
+
+            // Load all suitable solvers from current assembly. Below in this example, there is user-defined solver
+            // "class UserBiCgStab : IIterativeSolverSetup<double>" which uses regular BiCgStab solver. But user may create any other solver 
+            // and solver setup classes which implement IIterativeSolverSetup<T> and pass assembly to next function:
+            CompositeSolver.LoadSolverInformationFromAssembly(Assembly.GetExecutingAssembly());
+
+            // Create composite solver
+            var solver = new CompositeSolver(monitor);
+            
+            // 1. Solve the matrix equation
+            var resultX = solver.Solve(matrixA, vectorB);
+            Console.WriteLine(@"1. Solve the matrix equation");
+            Console.WriteLine();
+
+            // 2. Check solver status of the iterations. 
+            // Solver has property IterationResult which contains the status of the iteration once the calculation is finished.
+            // Possible values are:
+            // - CalculationCancelled: calculation was cancelled by the user;
+            // - CalculationConverged: calculation has converged to the desired convergence levels;
+            // - CalculationDiverged: calculation diverged;
+            // - CalculationFailure: calculation has failed for some reason;
+            // - CalculationIndetermined: calculation is indetermined, not started or stopped;
+            // - CalculationRunning: calculation is running and no results are yet known;
+            // - CalculationStoppedWithoutConvergence: calculation has been stopped due to reaching the stopping limits, but that convergence was not achieved;
+            Console.WriteLine(@"2. Solver status of the iterations");
+            Console.WriteLine(solver.IterationResult);
+            Console.WriteLine();
+
+            // 3. Solution result vector of the matrix equation
+            Console.WriteLine(@"3. Solution result vector of the matrix equation");
+            Console.WriteLine(resultX.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // 4. Verify result. Multiply coefficient matrix "A" by result vector "x"
+            var reconstructVecorB = matrixA * resultX;
+            Console.WriteLine(@"4. Multiply coefficient matrix 'A' by result vector 'x'");
+            Console.WriteLine(reconstructVecorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+        }
+    }
+
+    /// <summary>
+    /// Sample of user-defined solver setup
+    /// </summary>
+    public class UserBiCgStab : IIterativeSolverSetup<double>
+    {
+        /// <summary>
+        /// Gets the type of the solver that will be created by this setup object.
+        /// </summary>
+        public Type SolverType
+        {
+            get
+            {
+                return null;
+            }
+        }
+
+        /// <summary>
+        /// Gets type of preconditioner, if any, that will be created by this setup object.
+        /// </summary>
+        public Type PreconditionerType
+        {
+            get
+            {
+                return null;
+            }
+        }
+
+        /// <summary>
+        /// Creates a fully functional iterative solver with the default settings
+        /// given by this setup.
+        /// </summary>
+        /// <returns>A new <see cref="IIterativeSolver{T}"/>.</returns>
+        public IIterativeSolver<double> CreateNew()
+        {
+            return new BiCgStab();
+        }
+
+        /// <summary>
+        /// Gets the relative speed of the solver. 
+        /// </summary>
+        /// <value>Returns a value between 0 and 1, inclusive.</value>
+        public double SolutionSpeed
+        {
+            get
+            {
+                return 0.99;
+            }
+        }
+
+        /// <summary>
+        /// Gets the relative reliability of the solver.
+        /// </summary>
+        /// <value>Returns a value between 0 and 1 inclusive.</value>
+        public double Reliability
+        {
+            get
+            {
+                return 0.99;
+            }
+        }
+    }
+}

src/Examples/LinearAlgebra/IterativeSolvers/GpBiCgSolver.cs

@@ -0,0 +1,139 @@
+// <copyright file="GpBiCgSolver.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.LinearAlgebra.IterativeSolvers
+{
+    using System;
+    using System.Globalization;
+    using MathNet.Numerics.LinearAlgebra.Double;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.StopCriterium;
+    using MathNet.Numerics.LinearAlgebra.Generic.Solvers.StopCriterium;
+
+    /// <summary>
+    /// GpBiCg Iterative solver
+    /// </summary>
+    public class GpBiCgSolver : IExample
+    {
+         /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Generalized Product Bi-Conjugate Gradient iterative solver";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Solve linear equation using Generalized Product Bi-Conjugate Gradient (GPBiCG) solver";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // Format matrix output to console
+            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
+            formatProvider.TextInfo.ListSeparator = " ";
+
+            // Solve next system of linear equations (Ax=b):
+            // 5*x + 2*y - 4*z = -7
+            // 3*x - 7*y + 6*z = 38
+            // 4*x + 1*y + 5*z = 43
+
+            // Create matrix "A" with coefficients 
+            var matrixA = new DenseMatrix(new[,] { { 5.00, 2.00, -4.00 }, { 3.00, -7.00, 6.00 }, { 4.00, 1.00, 5.00 } });
+            Console.WriteLine(@"Matrix 'A' with coefficients");
+            Console.WriteLine(matrixA.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create vector "b" with the constant terms.
+            var vectorB = new DenseVector(new[] { -7.0, 38.0, 43.0 });
+            Console.WriteLine(@"Vector 'b' with the constant terms");
+            Console.WriteLine(vectorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create stop criteriums to monitor an iterative calculation. There are next available stop criteriums:
+            // - DivergenceStopCriterium: monitors an iterative calculation for signs of divergence;
+            // - FailureStopCriterium: monitors residuals for NaN's;
+            // - IterationCountStopCriterium: monitors the numbers of iteration steps;
+            // - ResidualStopCriterium: monitors residuals if calculation is considered converged;
+
+            // Stop calculation if 1000 iterations reached during calculation
+            var iterationCountStopCriterium = new IterationCountStopCriterium(1000);
+
+            // Stop calculation if residuals are below 1E-10 --> the calculation is considered converged
+            var residualStopCriterium = new ResidualStopCriterium(1e-10);
+
+            // Create monitor with defined stop criteriums
+            var monitor = new Iterator(new IIterationStopCriterium<double>[] { iterationCountStopCriterium, residualStopCriterium });
+
+            // Create Generalized Product Bi-Conjugate Gradient solver
+            var solver = new GpBiCg(monitor);
+
+            // 1. Solve the matrix equation
+            var resultX = solver.Solve(matrixA, vectorB);
+            Console.WriteLine(@"1. Solve the matrix equation");
+            Console.WriteLine();
+
+            // 2. Check solver status of the iterations. 
+            // Solver has property IterationResult which contains the status of the iteration once the calculation is finished.
+            // Possible values are:
+            // - CalculationCancelled: calculation was cancelled by the user;
+            // - CalculationConverged: calculation has converged to the desired convergence levels;
+            // - CalculationDiverged: calculation diverged;
+            // - CalculationFailure: calculation has failed for some reason;
+            // - CalculationIndetermined: calculation is indetermined, not started or stopped;
+            // - CalculationRunning: calculation is running and no results are yet known;
+            // - CalculationStoppedWithoutConvergence: calculation has been stopped due to reaching the stopping limits, but that convergence was not achieved;
+            Console.WriteLine(@"2. Solver status of the iterations");
+            Console.WriteLine(solver.IterationResult);
+            Console.WriteLine();
+
+            // 3. Solution result vector of the matrix equation
+            Console.WriteLine(@"3. Solution result vector of the matrix equation");
+            Console.WriteLine(resultX.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // 4. Verify result. Multiply coefficient matrix "A" by result vector "x"
+            var reconstructVecorB = matrixA * resultX;
+            Console.WriteLine(@"4. Multiply coefficient matrix 'A' by result vector 'x'");
+            Console.WriteLine(reconstructVecorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/LinearAlgebra/IterativeSolvers/MlkBiCgStabSolver.cs

@@ -0,0 +1,140 @@
+// <copyright file="MlkBiCgStabSolver.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.LinearAlgebra.IterativeSolvers
+{
+    using System;
+    using System.Globalization;
+    using MathNet.Numerics.LinearAlgebra.Double;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.StopCriterium;
+    using MathNet.Numerics.LinearAlgebra.Generic.Solvers.StopCriterium;
+
+    /// <summary>
+    /// Multiple-Lanczos Bi-Conjugate Gradient stabilized Iterative solver
+    /// </summary>
+    /// <seealso cref="http://en.wikipedia.org/wiki/Derivation_of_the_conjugate_gradient_method#Derivation_from_the_Arnoldi.2FLanczos_iteration"/>
+    public class MlkBiCgStabSolver : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Multiple-Lanczos Bi-Conjugate Gradient Stabilized iterative solver";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Solve linear equation using Multiple-Lanczos Bi-Conjugate Gradient stabilized (ML(k)-BiCGStab) solver";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // Format matrix output to console
+            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
+            formatProvider.TextInfo.ListSeparator = " ";
+
+            // Solve next system of linear equations (Ax=b):
+            // 5*x + 2*y - 4*z = -7
+            // 3*x - 7*y + 6*z = 38
+            // 4*x + 1*y + 5*z = 43
+
+            // Create matrix "A" with coefficients 
+            var matrixA = new DenseMatrix(new[,] { { 5.00, 2.00, -4.00 }, { 3.00, -7.00, 6.00 }, { 4.00, 1.00, 5.00 } });
+            Console.WriteLine(@"Matrix 'A' with coefficients");
+            Console.WriteLine(matrixA.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create vector "b" with the constant terms.
+            var vectorB = new DenseVector(new[] { -7.0, 38.0, 43.0 });
+            Console.WriteLine(@"Vector 'b' with the constant terms");
+            Console.WriteLine(vectorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create stop criteriums to monitor an iterative calculation. There are next available stop criteriums:
+            // - DivergenceStopCriterium: monitors an iterative calculation for signs of divergence;
+            // - FailureStopCriterium: monitors residuals for NaN's;
+            // - IterationCountStopCriterium: monitors the numbers of iteration steps;
+            // - ResidualStopCriterium: monitors residuals if calculation is considered converged;
+
+            // Stop calculation if 1000 iterations reached during calculation
+            var iterationCountStopCriterium = new IterationCountStopCriterium(1000);
+
+            // Stop calculation if residuals are below 1E-10 --> the calculation is considered converged
+            var residualStopCriterium = new ResidualStopCriterium(1e-10);
+
+            // Create monitor with defined stop criteriums
+            var monitor = new Iterator(new IIterationStopCriterium<double>[] { iterationCountStopCriterium, residualStopCriterium });
+
+            // Create Multiple-Lanczos Bi-Conjugate Gradient Stabilized solver
+            var solver = new MlkBiCgStab(monitor);
+
+            // 1. Solve the matrix equation
+            var resultX = solver.Solve(matrixA, vectorB);
+            Console.WriteLine(@"1. Solve the matrix equation");
+            Console.WriteLine();
+
+            // 2. Check solver status of the iterations. 
+            // Solver has property IterationResult which contains the status of the iteration once the calculation is finished.
+            // Possible values are:
+            // - CalculationCancelled: calculation was cancelled by the user;
+            // - CalculationConverged: calculation has converged to the desired convergence levels;
+            // - CalculationDiverged: calculation diverged;
+            // - CalculationFailure: calculation has failed for some reason;
+            // - CalculationIndetermined: calculation is indetermined, not started or stopped;
+            // - CalculationRunning: calculation is running and no results are yet known;
+            // - CalculationStoppedWithoutConvergence: calculation has been stopped due to reaching the stopping limits, but that convergence was not achieved;
+            Console.WriteLine(@"2. Solver status of the iterations");
+            Console.WriteLine(solver.IterationResult);
+            Console.WriteLine();
+
+            // 3. Solution result vector of the matrix equation
+            Console.WriteLine(@"3. Solution result vector of the matrix equation");
+            Console.WriteLine(resultX.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // 4. Verify result. Multiply coefficient matrix "A" by result vector "x"
+            var reconstructVecorB = matrixA * resultX;
+            Console.WriteLine(@"4. Multiply coefficient matrix 'A' by result vector 'x'");
+            Console.WriteLine(reconstructVecorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/LinearAlgebra/IterativeSolvers/TFQMRSolver.cs

@@ -0,0 +1,140 @@
+// <copyright file="TFQMRSolver.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.LinearAlgebra.IterativeSolvers
+{
+    using System;
+    using System.Globalization;
+    using MathNet.Numerics.LinearAlgebra.Double;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative;
+    using MathNet.Numerics.LinearAlgebra.Double.Solvers.StopCriterium;
+    using MathNet.Numerics.LinearAlgebra.Generic.Solvers.StopCriterium;
+
+    /// <summary>
+    /// Transpose Free Quasi-Minimal Residual iterative solver
+    /// </summary>
+    /// <seealso cref="http://es.wikipedia.org/wiki/Algoritmo_TFQMR"/>
+    public class TFQMRSolver : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Transpose Free Quasi-Minimal Residual iterative solver";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Solve linear equation using Transpose Free Quasi-Minimal Residual (TFQMR) iterative solver";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // Format matrix output to console
+            var formatProvider = (CultureInfo)CultureInfo.InvariantCulture.Clone();
+            formatProvider.TextInfo.ListSeparator = " ";
+
+            // Solve next system of linear equations (Ax=b):
+            // 5*x + 2*y - 4*z = -7
+            // 3*x - 7*y + 6*z = 38
+            // 4*x + 1*y + 5*z = 43
+
+            // Create matrix "A" with coefficients 
+            var matrixA = new DenseMatrix(new[,] { { 5.00, 2.00, -4.00 }, { 3.00, -7.00, 6.00 }, { 4.00, 1.00, 5.00 } });
+            Console.WriteLine(@"Matrix 'A' with coefficients");
+            Console.WriteLine(matrixA.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create vector "b" with the constant terms.
+            var vectorB = new DenseVector(new[] { -7.0, 38.0, 43.0 });
+            Console.WriteLine(@"Vector 'b' with the constant terms");
+            Console.WriteLine(vectorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // Create stop criteriums to monitor an iterative calculation. There are next available stop criteriums:
+            // - DivergenceStopCriterium: monitors an iterative calculation for signs of divergence;
+            // - FailureStopCriterium: monitors residuals for NaN's;
+            // - IterationCountStopCriterium: monitors the numbers of iteration steps;
+            // - ResidualStopCriterium: monitors residuals if calculation is considered converged;
+
+            // Stop calculation if 1000 iterations reached during calculation
+            var iterationCountStopCriterium = new IterationCountStopCriterium(1000);
+
+            // Stop calculation if residuals are below 1E-10 --> the calculation is considered converged
+            var residualStopCriterium = new ResidualStopCriterium(1e-10);
+
+            // Create monitor with defined stop criteriums
+            var monitor = new Iterator(new IIterationStopCriterium<double>[] { iterationCountStopCriterium, residualStopCriterium });
+
+            // Create Transpose Free Quasi-Minimal Residual solver
+            var solver = new TFQMR(monitor);
+
+            // 1. Solve the matrix equation
+            var resultX = solver.Solve(matrixA, vectorB);
+            Console.WriteLine(@"1. Solve the matrix equation");
+            Console.WriteLine();
+
+            // 2. Check solver status of the iterations. 
+            // Solver has property IterationResult which contains the status of the iteration once the calculation is finished.
+            // Possible values are:
+            // - CalculationCancelled: calculation was cancelled by the user;
+            // - CalculationConverged: calculation has converged to the desired convergence levels;
+            // - CalculationDiverged: calculation diverged;
+            // - CalculationFailure: calculation has failed for some reason;
+            // - CalculationIndetermined: calculation is indetermined, not started or stopped;
+            // - CalculationRunning: calculation is running and no results are yet known;
+            // - CalculationStoppedWithoutConvergence: calculation has been stopped due to reaching the stopping limits, but that convergence was not achieved;
+            Console.WriteLine(@"2. Solver status of the iterations");
+            Console.WriteLine(solver.IterationResult);
+            Console.WriteLine();
+
+            // 3. Solution result vector of the matrix equation
+            Console.WriteLine(@"3. Solution result vector of the matrix equation");
+            Console.WriteLine(resultX.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+
+            // 4. Verify result. Multiply coefficient matrix "A" by result vector "x"
+            var reconstructVecorB = matrixA * resultX;
+            Console.WriteLine(@"4. Multiply coefficient matrix 'A' by result vector 'x'");
+            Console.WriteLine(reconstructVecorB.ToString("#0.00\t", formatProvider));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/NumberTheory.cs

@@ -0,0 +1,117 @@
+// <copyright file="NumberTheory.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples
+{
+    using System;
+    using MathNet.Numerics.NumberTheory;
+
+    /// <summary>
+    /// Number theory utility functions
+    /// </summary>
+    public class NumberTheory : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Number theory utility functions";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Usage of the number theory utility functions and extention methods";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // 1. Find out whether the provided number is an even number
+            Console.WriteLine(@"1. Find out whether the provided number is an even number");
+            Console.WriteLine(@"{0} is even = {1}. {2} is even = {3}", 1, IntegerTheory.IsEven(1), 2, 2.IsEven());
+            Console.WriteLine();
+
+            // 2. Find out whether the provided number is an odd number
+            Console.WriteLine(@"2. Find out whether the provided number is an odd number");
+            Console.WriteLine(@"{0} is odd = {1}. {2} is odd = {3}", 1, 1.IsOdd(), 2, IntegerTheory.IsOdd(2));
+            Console.WriteLine();
+
+            // 3. Find out whether the provided number is a perfect power of two
+            Console.WriteLine(@"2. Find out whether the provided number is a perfect power of two");
+            Console.WriteLine(@"{0} is power of two = {1}. {2} is power of two = {3}", 5, 5.IsPowerOfTwo(), 16, IntegerTheory.IsPowerOfTwo(16));
+            Console.WriteLine();
+
+            // 4. Find the closest perfect power of two that is larger or equal to 97
+            Console.WriteLine(@"4. Find the closest perfect power of two that is larger or equal to 97");
+            Console.WriteLine(97.CeilingToPowerOfTwo());
+            Console.WriteLine();
+
+            // 5. Raise 2 to the 16
+            Console.WriteLine(@"5. Raise 2 to the 16");
+            Console.WriteLine(16.PowerOfTwo());
+            Console.WriteLine();
+
+            // 6. Find out whether the number is a perfect square
+            Console.WriteLine(@"6. Find out whether the number is a perfect square");
+            Console.WriteLine(@"{0} is perfect square = {1}. {2} is perfect square = {3}", 37, 37.IsPerfectSquare(), 81, IntegerTheory.IsPerfectSquare(81));
+            Console.WriteLine();
+
+            // 7. Compute the greatest common divisor of 32 and 36 
+            Console.WriteLine(@"7. Returns the greatest common divisor of 32 and 36");
+            Console.WriteLine(IntegerTheory.GreatestCommonDivisor(32, 36));
+            Console.WriteLine();
+
+            // 8. Compute the greatest common divisor of 492, -984, 123, 246
+            Console.WriteLine(@"8. Returns the greatest common divisor of 492, -984, 123, 246");
+            Console.WriteLine(IntegerTheory.GreatestCommonDivisor(492, -984, 123, 246));
+            Console.WriteLine();
+
+            // 9. Compute the extended greatest common divisor "z", such that 45*x + 18*y = z
+            Console.WriteLine(@"9. Compute the extended greatest common divisor Z, such that 45*x + 18*y = Z");
+            long x, y;
+            var z = IntegerTheory.ExtendedGreatestCommonDivisor(45, 18, out x, out y);
+            Console.WriteLine(@"z = {0}, x = {1}, y = {2}. 45*{1} + 18*{2} = {0}", z, x, y);
+            Console.WriteLine();
+
+            // 10. Compute the least common multiple of 16 and 12
+            Console.WriteLine(@"10. Compute the least common multiple of 16 and 12");
+            Console.WriteLine(IntegerTheory.LeastCommonMultiple(16, 12));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/RandomNumberGeneration.cs

@@ -0,0 +1,191 @@
+// <copyright file="RandomNumberGeneration.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples
+{
+    using System;
+    using MathNet.Numerics.Random;
+
+    /// <summary>
+    /// Random number generation
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/tutorial/RandomNumberGeneration.html">Random number generation</seealso>
+    public class RandomNumberGeneration : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Random number generation";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Usage examples of random number generators (RNG)";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Random_number_generation">Random number generation</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Linear_congruential_generator">Linear congruential generator</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Mersenne_twister">Mersenne twister</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Lagged_Fibonacci_generator">Lagged Fibonacci generator</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Xorshift">Xorshift</seealso>
+        public void Run()
+        {
+            // All RNG classes in MathNet have next counstructors:
+            // - RNG(int seed, bool threadSafe): initializes a new instance with specific seed value and thread safe property
+            // - RNG(int seed): iуууnitializes a new instance with specific seed value. Thread safe property is set to Control.ThreadSafeRandomNumberGenerators
+            // - RNG(bool threadSafe) : initializes a new instance with the seed value set to DateTime.Now.Ticks and specific thread safe property
+            // - RNG(bool threadSafe) : initializes a new instance with the seed value set to DateTime.Now.Ticks and thread safe property set to Control.ThreadSafeRandomNumberGenerators
+
+            // All RNG classes in MathNet have next methods to produce random values:
+            // - double[] NextDouble(int n): returns an "n"-size array of uniformly distributed random doubles in the interval [0.0,1.0];
+            // - int Next(): returns a nonnegative random number;
+            // - int Next(int maxValue): returns a random number less then a specified maximum;
+            // - int Next(int minValue, int maxValue): returns a random number within a specified range;
+            // - void NextBytes(byte[] buffer): fills the elements of a specified array of bytes with random numbers;
+
+            // All RNG classes in MathNet have next extension methods to produce random values:
+            // - long NextInt64(): returns a nonnegative random number less than "Int64.MaxValue";
+            // - int NextFullRangeInt32(): returns a random number of the full Int32 range;
+            // - long NextFullRangeInt64(): returns a random number of the full Int64 range;
+            // - decimal NextDecimal(): returns a nonnegative decimal floating point random number less than 1.0;
+
+            // 1. Multiplicative congruential generator using a modulus of 2^31-1 and a multiplier of 1132489760
+            var mcg31M1 = new Mcg31m1(1);
+            Console.WriteLine(@"1. Generate 10 random double values using Multiplicative congruential generator with a modulus of 2^31-1 and a multiplier of 1132489760");
+            var randomValues = mcg31M1.NextDouble(10);
+            for (var i = 0; i < randomValues.Length; i++)
+            {
+                Console.Write(randomValues[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 2. Multiplicative congruential generator using a modulus of 2^59 and a multiplier of 13^13
+            var mcg59 = new Mcg59(1);
+            Console.WriteLine(@"2. Generate 10 random integer values using Multiplicative congruential generator with a modulus of 2^59 and a multiplier of 13^13");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(mcg59.Next() + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 3. Random number generator using Mersenne Twister 19937 algorithm
+            var mersenneTwister = new MersenneTwister(1);
+            Console.WriteLine(@"3. Generate 10 random integer values less then 100 using Mersenne Twister 19937 algorithm");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(mersenneTwister.Next(100) + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Multiple recursive generator with 2 components of order 3
+            var mrg32K3A = new Mrg32k3a(1);
+            Console.WriteLine(@"4. Generate 10 random integer values in range [50;100] using multiple recursive generator with 2 components of order 3");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(mrg32K3A.Next(50, 100) + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 5. Parallel Additive Lagged Fibonacci pseudo-random number generator
+            var palf = new Palf(1);
+            Console.WriteLine(@"5. Generate 10 random bytes using Parallel Additive Lagged Fibonacci pseudo-random number generator");
+            var bytes = new byte[10];
+            palf.NextBytes(bytes);
+            for (var i = 0; i < bytes.Length; i++)
+            {
+                Console.Write(bytes[i] + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 6. A random number generator based on the "System.Security.Cryptography.RandomNumberGenerator" class in the .NET library
+            var systemCryptoRandomNumberGenerator = new SystemCryptoRandomNumberGenerator();
+            Console.WriteLine(@"6. Generate 10 random decimal values using RNG based on the 'System.Security.Cryptography.RandomNumberGenerator'");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(systemCryptoRandomNumberGenerator.NextDecimal().ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 7. Wichmann-Hill’s 1982 combined multiplicative congruential generator
+            var rngWh1982 = new WH1982();
+            Console.WriteLine(@"7. Generate 10 random full Int32 range values using Wichmann-Hill’s 1982 combined multiplicative congruential generator");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(rngWh1982.NextFullRangeInt32() + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 8. Wichmann-Hill’s 2006 combined multiplicative congruential generator.
+            var rngWh2006 = new WH2006();
+            Console.WriteLine(@"8. Generate 10 random full Int64 range values using Wichmann-Hill’s 2006 combined multiplicative congruential generator");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(rngWh2006.NextFullRangeInt32() + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 9. Multiply-with-carry Xorshift pseudo random number generator
+            var xorshift = new Xorshift();
+            Console.WriteLine(@"9. Generate 10 random nonnegative values less than Int64.MaxValue using Multiply-with-carry Xorshift pseudo random number generator");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(xorshift.NextInt64() + @" ");
+            }
+
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/Sampling/Chebyshev.cs

@@ -0,0 +1,96 @@
+// <copyright file="Chebyshev.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.Sampling
+{
+    using System;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Example of generic function sampling and quantization provider
+    /// </summary>
+    public class Chebyshev : IExample
+    {
+              /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Sampling - Chebyshev";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Samples a function at the roots of the Chebyshev polynomial";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // 1. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the first kind within interval [0, 10] 
+            var result = Sample.ChebyshevNodesFirstKind(Function, 0, 10, 20);
+            Console.WriteLine(@"1. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the first kind within interval [0, 10]");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 2. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the second kind within interval [0, 10]
+            result = Sample.ChebyshevNodesSecondKind(Function, 0, 10, 20);
+            Console.WriteLine(@"2. Get 20 samples of f(x) = (x * x) / 2 at the roots of the Chebyshev polynomial of the second kind within interval [0, 10]");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Fucntion f(x) = (x * x) / 2
+        /// </summary>
+        /// <param name="x">Input value</param>
+        /// <returns>Calculation result</returns>
+        public double Function(double x)
+        {
+            return Math.Pow(x, 2) / 2;
+        }
+    }
+}

src/Examples/Sampling/Equidistant.cs

@@ -0,0 +1,127 @@
+// <copyright file="Equidistant.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.Sampling
+{
+    using System;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Example of generic function sampling and quantization provider
+    /// </summary>
+    public class Equidistant : IExample
+    {
+              /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Sampling - Equidistant";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Samples a function equidistant";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // 1. Get 11 samples of f(x) = (x * x) / 2 equidistant within interval [-5, 5] 
+            var result = Sample.EquidistantInterval(Function, -5, 5, 11);
+            Console.WriteLine(@"1. Get 11 samples of f(x) = (x * x) / 2 equidistant within interval [-5, 5]");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 2. Get 10 samples of f(x) = (x * x) / 2 equidistant starting at x=1 with step = 0.5 and retrieve sample points
+            double[] samplePoints;
+            result = Sample.EquidistantStartingAt(Function, 1, 0.5, 10, out samplePoints);
+            Console.WriteLine(@"2. Get 10 samples of f(x) = (x * x) / 2 equidistant starting at x=1 with step = 0.5 and retrieve sample points");
+            Console.Write(@"Points: ");
+            for (var i = 0; i < samplePoints.Length; i++)
+            {
+                Console.Write(samplePoints[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.Write(@"Values: ");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 3. Get 10 samples of f(x) = (x * x) / 2 equidistant within period = 10 and period offset = 5
+            result = Sample.EquidistantPeriodic(Function, 10, 5, 10);
+            Console.WriteLine(@"3. Get 10 samples of f(x) = (x * x) / 2 equidistant within period = 10 and period offset = 5");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 4. Sample f(x) = (x * x) / 2 equidistant  to an integer-domain function starting at x = 0 and step = 2
+            var equidistant = Sample.EquidistantToFunction(Function, 0, 2);
+            Console.WriteLine(@" 4. Sample f(x) = (x * x) / 2 equidistant  to an integer-domain function starting at x = 0 and step = 2");
+            for (var i = 0; i < 10; i++)
+            {
+                Console.Write(equidistant(i).ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Fucntion f(x) = (x * x) / 2
+        /// </summary>
+        /// <param name="x">Input value</param>
+        /// <returns>Calculation result</returns>
+        public double Function(double x)
+        {
+            return Math.Pow(x, 2) / 2;
+        }
+    }
+}

src/Examples/Sampling/Random.cs

@@ -0,0 +1,131 @@
+// <copyright file="Random.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples.Sampling
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Example of generic function sampling and quantization provider
+    /// </summary>
+    public class Random : IExample
+    {
+              /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Sampling - Random";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Samples a function randomly with the provided distribution";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        public void Run()
+        {
+            // 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]
+            var uniform = new ContinuousUniform(-10, 10);
+            var result = Sample.Random(Function, uniform, 10);
+            Console.WriteLine(@" 1. Get 10 random samples of f(x) = (x * x) / 2 using continuous uniform distribution on [-10, 10]");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points
+            var exponential = new Exponential(1);
+            double[] samplePoints;
+            result = Sample.Random(Function, exponential, 10, out samplePoints);
+            Console.WriteLine(@"2. Get 10 random samples of f(x) = (x * x) / 2 using Exponential(1) distribution and retrieve sample points");
+            Console.Write(@"Points: ");
+            for (var i = 0; i < samplePoints.Length; i++)
+            {
+                Console.Write(samplePoints[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.Write(@"Values: ");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution
+            var chiSquare = new ChiSquare(10);
+            result = Sample.Random(TwoDomainFunction, chiSquare, 10);
+            Console.WriteLine(@" 3. Get 10 random samples of f(x, y) = (x * y) / 2 using ChiSquare(10) distribution");
+            for (var i = 0; i < result.Length; i++)
+            {
+                Console.Write(result[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+        }
+
+        /// <summary>
+        /// Fucntion f(x, y) = (x * y) / 2
+        /// </summary>
+        /// <param name="x">Input value</param>
+        /// <returns>Calculation result</returns>
+        public double Function(double x)
+        {
+            return Math.Pow(x, 2) / 2;
+        }
+
+        /// <summary>
+        /// Fucntion f(x,y) = (x * y) / 2
+        /// </summary>
+        /// <param name="x">X input value</param>
+        /// <param name="y">Y input value</param>
+        /// <returns>Calculation result</returns>
+        public double TwoDomainFunction(double x, double y)
+        {
+            return (x * y) / 2;
+        }
+    }
+}

src/Examples/SpecialFunctions/Beta.cs

@@ -0,0 +1,96 @@
+// <copyright file="Beta.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+namespace Examples.SpecialFunctions
+{
+    using System;
+    using MathNet.Numerics;
+
+    /// <summary>
+    /// Special Functions: Beta
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Beta.html"/>
+    public class Beta : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Special Functions: Beta";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Beta, incomplete Beta, regularized Beta";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Beta_function">Beta function</seealso>
+        public void Run()
+        {
+            // 1. Compute the Beta function at z = 1.0, w = 3.0
+            Console.WriteLine(@"1. Compute the Beta function at z = 1.0, w = 3.0");
+            Console.WriteLine(SpecialFunctions.Beta(1.0, 3.0));
+            Console.WriteLine();
+
+            // 2. Compute the logarithm of the Beta function at z = 1.0, w = 3.0
+            Console.WriteLine(@"2. Compute the logarithm of the Beta function at z = 1.0, w = 3.0");
+            Console.WriteLine(SpecialFunctions.BetaLn(1.0, 3.0));
+            Console.WriteLine();
+
+            // 3. Compute the Beta incomplete function at z = 1.0, w = 3.0, x = 0.7 
+            Console.WriteLine(@"3. Compute the Beta incomplete function at z = 1.0, w = 3.0, x = 0.7");
+            Console.WriteLine(SpecialFunctions.BetaIncomplete(1.0, 3.0, 0.7));
+            Console.WriteLine();
+
+            // 4. Compute the Beta incomplete function at z = 1.0, w = 3.0, x = 1.0 
+            Console.WriteLine(@"4. Compute the Beta incomplete function at z = 1.0, w = 3.0, x = 1.0");
+            Console.WriteLine(SpecialFunctions.BetaIncomplete(1.0, 3.0, 1.0));
+            Console.WriteLine();
+
+            // 5. Compute the Beta regularized function at z = 1.0, w = 3.0, x = 0.7
+            Console.WriteLine(@"5. Compute the Beta regularized function at z = 1.0, w = 3.0, x = 0.7");
+            Console.WriteLine(SpecialFunctions.BetaRegularized(1.0, 3.0, 0.7));
+            Console.WriteLine();
+
+            // 6. Compute the Beta regularized  function at z = 1.0, w = 3.0, x = 1.0 
+            Console.WriteLine(@"6. Compute the Beta regularized function at z = 1.0, w = 3.0, x = 1.0");
+            Console.WriteLine(SpecialFunctions.BetaRegularized(1.0, 3.0, 1.0));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/SpecialFunctions/Common.cs

@@ -0,0 +1,101 @@
+// <copyright file="Common.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+namespace Examples.SpecialFunctions
+{
+    using System;
+    using MathNet.Numerics;
+
+    /// <summary>
+    /// Special Functions
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/PolyGamma.html"/>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/HarmonicNumber.html"/>
+    public class Common : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Special Functions";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Harmonic, DiGamma, Logit, Logistic";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Digamma_function">Digamma function</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Harmonic_number">Harmonic number</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Harmonic_number#Generalized_harmonic_numbers">Generalized harmonic numbers</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Logistic_function">Logistic function</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Logit">Logit function</seealso>
+        public void Run()
+        {
+            // 1. Calculate the Digamma function at point 5.0
+            Console.WriteLine(@"1. Calculate the Digamma function at point 5.0");
+            Console.WriteLine(SpecialFunctions.DiGamma(5.0));
+            Console.WriteLine();
+
+            // 2. Calculate the inverse Digamma function at point 1.5
+            Console.WriteLine(@"2. Calculate the inverse Digamma function at point 1.5");
+            Console.WriteLine(SpecialFunctions.DiGammaInv(1.5));
+            Console.WriteLine();
+
+            // 3. Calculate the 10'th Harmonic number
+            Console.WriteLine(@"3. Calculate the 10'th Harmonic number");
+            Console.WriteLine(SpecialFunctions.Harmonic(10));
+            Console.WriteLine();
+
+            // 4. Calculate the generalized harmonic number of order 10 of 3.0.
+            Console.WriteLine(@"4. Calculate the generalized harmonic number of order 10 of 3.0");
+            Console.WriteLine(SpecialFunctions.GeneralHarmonic(10, 3.0));
+            Console.WriteLine();
+
+            // 5. Calculate the logistic function of 3.0
+            Console.WriteLine(@"5. Calculate the logistic function of 3.0");
+            Console.WriteLine(SpecialFunctions.Logistic(3.0));
+            Console.WriteLine();
+
+            // 6. Calculate the logit function of 0.3
+            Console.WriteLine(@"6. Calculate the logit function of 0.3");
+            Console.WriteLine(SpecialFunctions.Logit(0.3));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/SpecialFunctions/ErrorFunction.cs

@@ -0,0 +1,129 @@
+// <copyright file="ErrorFunction.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+namespace Examples.SpecialFunctions
+{
+    using System;
+    using MathNet.Numerics;
+    using MathNet.Numerics.Sampling;
+
+    /// <summary>
+    /// Special Functions: error functions
+    /// </summary>
+    public class ErrorFunction : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Special Functions: error functions";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Error function (Gauss error function or probability integral)";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Error_function">Error function</seealso>
+        public void Run()
+        {
+            // 1. Calculate the error function at point 2
+            Console.WriteLine(@"1. Calculate the error function at point 2");
+            Console.WriteLine(SpecialFunctions.Erf(2));
+            Console.WriteLine();
+
+            // 2. Sample 10 values of the error function in [-1.0; 1.0]
+            Console.WriteLine(@"2. Sample 10 values of the error function in [-1.0; 1.0]");
+            var data = Sample.EquidistantInterval(SpecialFunctions.Erf, -1.0, 1.0, 10);
+            for (var i = 0; i < data.Length; i++)
+            {
+                Console.Write(data[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 3. Calculate the complementary error function at point 2
+            Console.WriteLine(@"3. Calculate the complementary error function at point 2");
+            Console.WriteLine(SpecialFunctions.Erfc(2));
+            Console.WriteLine();
+
+            // 4. Sample 10 values of the complementary error function in [-1.0; 1.0]
+            Console.WriteLine(@"4. Sample 10 values of the complementary error function in [-1.0; 1.0]");
+            data = Sample.EquidistantInterval(SpecialFunctions.Erfc, -1.0, 1.0, 10);
+            for (var i = 0; i < data.Length; i++)
+            {
+                Console.Write(data[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 5. Calculate the inverse error function at point z=0.5
+            Console.WriteLine(@"5. Calculate the inverse error function at point z=0.5");
+            Console.WriteLine(SpecialFunctions.ErfInv(0.5));
+            Console.WriteLine();
+
+            // 6. Sample 10 values of the inverse error function in [-1.0; 1.0]
+            Console.WriteLine(@"6. Sample 10 values of the inverse error function in [-1.0; 1.0]");
+            data = Sample.EquidistantInterval(SpecialFunctions.ErfInv, -1.0, 1.0, 10);
+            for (var i = 0; i < data.Length; i++)
+            {
+                Console.Write(data[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+            Console.WriteLine();
+
+            // 7. Calculate the complementary inverse error function at point z=0.5
+            Console.WriteLine(@"7. Calculate the complementary inverse error function at point z=0.5");
+            Console.WriteLine(SpecialFunctions.ErfcInv(0.5));
+            Console.WriteLine();
+
+            // 8. Sample 10 values of the complementary inverse error function in [-1.0; 1.0]
+            Console.WriteLine(@"8. Sample 10 values of the complementary inverse error function in [-1.0; 1.0]");
+            data = Sample.EquidistantInterval(SpecialFunctions.ErfcInv, -1.0, 1.0, 10);
+            for (var i = 0; i < data.Length; i++)
+            {
+                Console.Write(data[i].ToString("N") + @" ");
+            }
+
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/SpecialFunctions/Factorial.cs

@@ -0,0 +1,95 @@
+// <copyright file="Factorial.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+namespace Examples.SpecialFunctions
+{
+    using System;
+    using MathNet.Numerics;
+
+    /// <summary>
+    /// Special Functions: Factorial
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Factorial.html"/>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Binomial.html"/>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Multinomial.html"/>
+    public class Factorial : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Special Functions: Factorial";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Factorial, Binomial, Multinomial";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Factorial">Factorial</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Binomial_coefficient">Binomial coefficient</seealso>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Multinomial_theorem#Multinomial_coefficients">Multinomial coefficients</seealso>
+        public void Run()
+        {
+            // 1. Compute the factorial of 5
+            Console.WriteLine(@"1. Compute the factorial of 5");
+            Console.WriteLine(SpecialFunctions.Factorial(5).ToString("N"));
+            Console.WriteLine();
+
+            // 2. Compute the logarithm of the factorial of 5
+            Console.WriteLine(@"2. Compute the logarithm of the factorial of 5");
+            Console.WriteLine(SpecialFunctions.FactorialLn(5).ToString("N"));
+            Console.WriteLine();
+
+            // 3. Compute the binomial coefficient: 10 choose 8
+            Console.WriteLine(@"3. Compute the binomial coefficient: 10 choose 8");
+            Console.WriteLine(SpecialFunctions.Binomial(10, 8).ToString("N"));
+            Console.WriteLine();
+
+            // 4. Compute the logarithm of the binomial coefficient: 10 choose 8
+            Console.WriteLine(@"4. Compute the logarithm of the binomial coefficient: 10 choose 8");
+            Console.WriteLine(SpecialFunctions.BinomialLn(10, 8).ToString("N"));
+            Console.WriteLine();
+
+            // 5. Compute the multinomial coefficient: 10 choose 2, 3, 5 
+            Console.WriteLine(@"5. Compute the multinomial coefficient: 10 choose 2, 3, 5");
+            Console.WriteLine(SpecialFunctions.Multinomial(10, new[] { 2, 3, 5 }).ToString("N"));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/SpecialFunctions/Gamma.cs

@@ -0,0 +1,116 @@
+// <copyright file="Gamma.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+namespace Examples.SpecialFunctions
+{
+    using System;
+    using MathNet.Numerics;
+
+    /// <summary>
+    /// Special Functions: Gamma
+    /// </summary>
+    /// <seealso cref="http://reference.wolfram.com/mathematica/ref/Gamma.html"/>
+    public class Gamma : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Special Functions: Gamma";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Gamma, incomplete Gamma, regularized Gamma";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Gamma_function">Gamma function</seealso>
+        public void Run()
+        {
+            // 1. Compute the Gamma function of 10
+            Console.WriteLine(@"1. Compute the Gamma function of 10");
+            Console.WriteLine(SpecialFunctions.Gamma(10).ToString("N"));
+            Console.WriteLine();
+
+            // 2. Compute the logarithm of the Gamma function of 10
+            Console.WriteLine(@"2. Compute the logarithm of the Gamma function of 10");
+            Console.WriteLine(SpecialFunctions.GammaLn(10).ToString("N"));
+            Console.WriteLine();
+
+            // 3. Compute the lower incomplete gamma(a, x) function at a = 10, x = 14 
+            Console.WriteLine(@"3. Compute the lower incomplete gamma(a, x) function at a = 10, x = 14");
+            Console.WriteLine(SpecialFunctions.GammaLowerIncomplete(10, 14).ToString("N"));
+            Console.WriteLine();
+
+            // 4. Compute the lower incomplete gamma(a, x) function at a = 10, x = 100 
+            Console.WriteLine(@"4. Compute the lower incomplete gamma(a, x) function at a = 10, x = 100");
+            Console.WriteLine(SpecialFunctions.GammaLowerIncomplete(10, 100).ToString("N"));
+            Console.WriteLine();
+
+            // 5. Compute the upper incomplete gamma(a, x) function at a = 10, x = 0 
+            Console.WriteLine(@"5. Compute the upper incomplete gamma(a, x) function at a = 10, x = 0");
+            Console.WriteLine(SpecialFunctions.GammaUpperIncomplete(10, 0).ToString("N"));
+            Console.WriteLine();
+
+            // 6. Compute the upper incomplete gamma(a, x) function at a = 10, x = 10 
+            Console.WriteLine(@"6. Compute the upper incomplete gamma(a, x) function at a = 10, x = 100");
+            Console.WriteLine(SpecialFunctions.GammaLowerIncomplete(10, 10).ToString("N"));
+            Console.WriteLine();
+
+            // 7. Compute the lower regularized gamma(a, x) function at a = 10, x = 14 
+            Console.WriteLine(@"7. Compute the lower regularized gamma(a, x) function at a = 10, x = 14");
+            Console.WriteLine(SpecialFunctions.GammaLowerRegularized(10, 14).ToString("N"));
+            Console.WriteLine();
+
+            // 8. Compute the lower regularized gamma(a, x) function at a = 10, x = 100 
+            Console.WriteLine(@"8. Compute the lower regularized gamma(a, x) function at a = 10, x = 100");
+            Console.WriteLine(SpecialFunctions.GammaLowerRegularized(10, 100).ToString("N"));
+            Console.WriteLine();
+
+            // 9. Compute the upper regularized gamma(a, x) function at a = 10, x = 0 
+            Console.WriteLine(@"9. Compute the upper regularized gamma(a, x) function at a = 10, x = 0");
+            Console.WriteLine(SpecialFunctions.GammaUpperRegularized(10, 0).ToString("N"));
+            Console.WriteLine();
+
+            // 10. Compute the upper regularized gamma(a, x) function at a = 10, x = 10 
+            Console.WriteLine(@"10. Compute the upper regularized gamma(a, x) function at a = 10, x = 100");
+            Console.WriteLine(SpecialFunctions.GammaUpperRegularized(10, 10).ToString("N"));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/SpecialFunctions/Stability.cs

@@ -0,0 +1,80 @@
+// <copyright file="Stability.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+namespace Examples.SpecialFunctions
+{
+    using System;
+    using MathNet.Numerics;
+
+    /// <summary>
+    /// Special Functions: Stability
+    /// </summary>
+    public class Stability : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Special Functions: Stability";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Exponential, Hypotenuse, Series";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Hypotenuse">Hypotenuse</seealso>
+        public void Run()
+        {
+            // 1. Compute numerically stable exponential of 10 minus one 
+            Console.WriteLine(@"1. Compute numerically stable exponential of 4.2876 minus one");
+            Console.WriteLine(SpecialFunctions.ExponentialMinusOne(4.2876));
+            Console.WriteLine();
+
+            // 2. Compute regular System.Math exponential of 15.28 minus one 
+            Console.WriteLine(@"2. Compute regular System.Math exponential of 4.2876 minus one ");
+            Console.WriteLine(Math.Exp(4.2876) - 1);
+            Console.WriteLine();
+
+            // 3. Compute numerically stable hypotenuse of a right angle triangle with a = 5, b = 3
+            Console.WriteLine(@"3. Compute numerically stable hypotenuse of a right angle triangle with a = 5, b = 3");
+            Console.WriteLine(SpecialFunctions.Hypotenuse(5, 3));
+            Console.WriteLine();
+        }
+    }
+}

src/Examples/Statistics.cs

@@ -0,0 +1,133 @@
+// <copyright file="Statistics.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+// http://mathnetnumerics.codeplex.com
+// Copyright (c) 2009-2010 Math.NET
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+// </copyright>
+
+namespace Examples
+{
+    using System;
+    using MathNet.Numerics.Distributions;
+    using MathNet.Numerics.Sampling;
+    using MathNet.Numerics.Statistics;
+
+    /// <summary>
+    /// Statistics on set of data
+    /// </summary>
+    public class Statistics : IExample
+    {
+        /// <summary>
+        /// Gets the name of this example
+        /// </summary>
+        public string Name
+        {
+            get
+            {
+                return "Statistics";
+            }
+        }
+
+        /// <summary>
+        /// Gets the description of this example
+        /// </summary>
+        public string Description
+        {
+            get
+            {
+                return "Basic statistics on set of data, correlation";
+            }
+        }
+
+        /// <summary>
+        /// Run example
+        /// </summary>
+        /// <seealso cref="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient">Pearson product-moment correlation coefficient</seealso>
+        public void Run()
+        {
+            // 1. Initialize the new instance of the ChiSquare distribution class with parameter dof = 5.
+            var chiSquare = new ChiSquare(5);
+            Console.WriteLine(@"1. Initialize the new instance of the ChiSquare distribution class with parameter DegreesOfFreedom = {0}", chiSquare.DegreesOfFreedom);
+            Console.WriteLine(@"{0} distributuion properties:", chiSquare);
+            Console.WriteLine(@"{0} - Largest element", chiSquare.Maximum.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Smallest element", chiSquare.Minimum.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Mean", chiSquare.Mean.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Median", chiSquare.Median.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Mode", chiSquare.Mode.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Variance", chiSquare.Variance.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Standard deviation", chiSquare.StdDev.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Skewness", chiSquare.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+            
+            // 2. Generate 1000 samples of the ChiSquare(5) distribution
+            Console.WriteLine(@"2. Generate 1000 samples of the ChiSquare(5) distribution");
+            var data = new double[1000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                data[i] = chiSquare.Sample();
+            }
+
+            // 3. Get basic statistics on set of generated data using extention methods
+            Console.WriteLine(@"3. Get basic statistics on set of generated data using extention methods");
+            Console.WriteLine(@"{0} - Largest element", data.Maximum().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Smallest element", data.Minimum().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Mean", data.Mean().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Median", data.Median().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Biased population variance", data.PopulationVariance().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Variance", data.Variance().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Standard deviation", data.StandardDeviation().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Biased sample standard deviation", data.PopulationStandardDeviation().ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // 4. Compute the basic statistics of data set using DescriptiveStatistics class
+            Console.WriteLine(@"4. Compute the basic statistics of data set using DescriptiveStatistics class");
+            var descriptiveStatistics = new DescriptiveStatistics(data);
+            Console.WriteLine(@"{0} - Kurtosis", descriptiveStatistics.Kurtosis.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Largest element", descriptiveStatistics.Maximum.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Smallest element", descriptiveStatistics.Minimum.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Mean", descriptiveStatistics.Mean.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Median", descriptiveStatistics.Median.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Variance", descriptiveStatistics.Variance.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Standard deviation", descriptiveStatistics.StandardDeviation.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine(@"{0} - Skewness", descriptiveStatistics.Skewness.ToString(" #0.00000;-#0.00000"));
+            Console.WriteLine();
+
+            // Generate 1000 samples of the ChiSquare(2.5) distribution
+            var chiSquareB = new ChiSquare(2);
+            var dataB = new double[1000];
+            for (var i = 0; i < data.Length; i++)
+            {
+                dataB[i] = chiSquareB.Sample();
+            }
+
+            // 5. Correlation coefficient between 1000 samples of ChiSquare(5) and ChiSquare(2.5)
+            Console.WriteLine(@"5. Correlation coefficient between 1000 samples of ChiSquare(5) and ChiSquare(2.5) is {0}", Correlation.Pearson(data, dataB).ToString("N04"));
+            Console.WriteLine();
+
+            // 6. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x
+            data = Sample.EquidistantInterval(x => x * 2, 0, 100, 1000);
+            dataB = Sample.EquidistantInterval(x => x * x, 0, 100, 1000);
+            Console.WriteLine(@"6. Correlation coefficient between 1000 samples of f(x) = x * 2 and f(x) = x * x is {0}", Correlation.Pearson(data, dataB).ToString("N04"));
+            Console.WriteLine();
+        }
+    }
+}

src/Numerics/Distributions/Continuous/Chi.cs

@@ -317,7 +317,7 @@ namespace MathNet.Numerics.Distributions
             var n = (int)_dof;
             for (var i = 0; i < n; i++)
             {
-                sum += Normal.Sample(rnd, 0.0, 1.0);
+                sum += Math.Pow(Normal.Sample(rnd, 0.0, 1.0), 2);
             }
 
             return Math.Sqrt(sum);

src/Numerics/Distributions/Continuous/ChiSquare.cs

@@ -278,7 +278,7 @@ namespace MathNet.Numerics.Distributions
             var n = (int)dof;
             for (var i = 0; i < n; i++)
             {
-                sum += Normal.Sample(rnd, 0.0, 1.0);
+                sum += Math.Pow(Normal.Sample(rnd, 0.0, 1.0), 2);
             }
 
             return sum;

src/Numerics/Distributions/Discrete/Hypergeometric.cs

@@ -366,8 +366,9 @@ namespace MathNet.Numerics.Distributions
                 }
 
                 size--;
+                n--;
             } 
-            while (1 < n);
+            while (0 < n);
 
             return x;
         }

src/Numerics/Interpolation/Algorithms/BulirschStoerRationalInterpolation.cs

@@ -168,7 +168,7 @@ namespace MathNet.Numerics.Interpolation.Algorithms
                     double ho = (_points[i] - t) * d[i] / hp;
 
                     double den = ho - c[i + 1];
-                    if (den == 0.0)
+                    if (den.AlmostEqual(0.0))
                     {
                         return double.NaN; // zero-div, singularity
                     }

src/Numerics/LinearAlgebra/Complex/Solvers/Iterative/CompositeSolver.cs

@@ -264,6 +264,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex.Solvers.Iterative
             var interfaceTypes = new List<Type>();
             foreach (var type in assembly.GetTypes().Where(type => (!type.IsAbstract && !type.IsEnum && !type.IsInterface && type.IsVisible)))
             {
+                interfaceTypes.Clear();
                 interfaceTypes.AddRange(type.GetInterfaces());
                 if (!interfaceTypes.Any(match => typeof(IIterativeSolverSetup<Complex>).IsAssignableFrom(match)))
                 {
@@ -512,6 +513,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex.Solvers.Iterative
                 if (_iterator.Status is CalculationConverged)
                 {
                     // We're done
+                    internalResult.CopyTo(result);
                     break;
                 }
 
@@ -522,7 +524,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex.Solvers.Iterative
                 {
                     // Copy the internal result to the result vector and
                     // continue with the calculation.
-                    internalInput.CopyTo(input);
+                    internalResult.CopyTo(result);
                 }
                 else
                 {

src/Numerics/LinearAlgebra/Complex32/Solvers/Iterative/CompositeSolver.cs

@@ -264,6 +264,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex32.Solvers.Iterative
             var interfaceTypes = new List<Type>();
             foreach (var type in assembly.GetTypes().Where(type => (!type.IsAbstract && !type.IsEnum && !type.IsInterface && type.IsVisible)))
             {
+                interfaceTypes.Clear();
                 interfaceTypes.AddRange(type.GetInterfaces());
                 if (!interfaceTypes.Any(match => typeof(IIterativeSolverSetup<Complex32>).IsAssignableFrom(match)))
                 {
@@ -512,6 +513,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex32.Solvers.Iterative
                 if (_iterator.Status is CalculationConverged)
                 {
                     // We're done
+                    internalResult.CopyTo(result);
                     break;
                 }
 
@@ -522,7 +524,7 @@ namespace MathNet.Numerics.LinearAlgebra.Complex32.Solvers.Iterative
                 {
                     // Copy the internal result to the result vector and
                     // continue with the calculation.
-                    internalInput.CopyTo(input);
+                    internalResult.CopyTo(result);
                 }
                 else
                 {

src/Numerics/LinearAlgebra/Double/Solvers/Iterative/CompositeSolver.cs

@@ -263,6 +263,7 @@ namespace MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative
             var interfaceTypes = new List<Type>();
             foreach (var type in assembly.GetTypes().Where(type => (!type.IsAbstract && !type.IsEnum && !type.IsInterface && type.IsVisible)))
             {
+                interfaceTypes.Clear();
                 interfaceTypes.AddRange(type.GetInterfaces());
                 if (!interfaceTypes.Any(match => typeof(IIterativeSolverSetup<double>).IsAssignableFrom(match)))
                 {
@@ -511,6 +512,7 @@ namespace MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative
                 if (_iterator.Status is CalculationConverged)
                 {
                     // We're done
+                    internalResult.CopyTo(result);
                     break;
                 }
 
@@ -521,7 +523,7 @@ namespace MathNet.Numerics.LinearAlgebra.Double.Solvers.Iterative
                 {
                     // Copy the internal result to the result vector and
                     // continue with the calculation.
-                    internalInput.CopyTo(input);
+                    internalResult.CopyTo(result);
                 }
                 else
                 {

src/Numerics/LinearAlgebra/Single/Solvers/Iterative/CompositeSolver.cs

@@ -263,6 +263,7 @@ namespace MathNet.Numerics.LinearAlgebra.Single.Solvers.Iterative
             var interfaceTypes = new List<Type>();
             foreach (var type in assembly.GetTypes().Where(type => (!type.IsAbstract && !type.IsEnum && !type.IsInterface && type.IsVisible)))
             {
+                interfaceTypes.Clear();
                 interfaceTypes.AddRange(type.GetInterfaces());
                 if (!interfaceTypes.Any(match => typeof(IIterativeSolverSetup<double>).IsAssignableFrom(match)))
                 {
@@ -511,6 +512,7 @@ namespace MathNet.Numerics.LinearAlgebra.Single.Solvers.Iterative
                 if (_iterator.Status is CalculationConverged)
                 {
                     // We're done
+                    internalResult.CopyTo(result);
                     break;
                 }
 
@@ -521,7 +523,7 @@ namespace MathNet.Numerics.LinearAlgebra.Single.Solvers.Iterative
                 {
                     // Copy the internal result to the result vector and
                     // continue with the calculation.
-                    internalInput.CopyTo(input);
+                    internalResult.CopyTo(result);
                 }
                 else
                 {

src/Numerics/NumberTheory/IntegerTheory.cs

@@ -193,7 +193,7 @@ namespace MathNet.Numerics.NumberTheory
         /// </summary>
         /// <param name="number">The number to very whether it's a perfect square.</param>
         /// <returns>True if and only if it is a perfect square.</returns>
-        public static bool IsPerfectSquare(int number)
+        public static bool IsPerfectSquare(this int number)
         {
             if (number < 0)
             {
@@ -220,7 +220,7 @@ namespace MathNet.Numerics.NumberTheory
         /// </summary>
         /// <param name="number">The number to very whether it's a perfect square.</param>
         /// <returns>True if and only if it is a perfect square.</returns>
-        public static bool IsPerfectSquare(long number)
+        public static bool IsPerfectSquare(this long number)
         {
             if (number < 0)
             {