diff --git a/src/Numerics/Distributions/Multivariate/NormalGamma.cs b/src/Numerics/Distributions/Multivariate/NormalGamma.cs
new file mode 100644
index 00000000..eff86d00
--- /dev/null
+++ b/src/Numerics/Distributions/Multivariate/NormalGamma.cs
@@ -0,0 +1,495 @@
+// <copyright file="NormalGamma.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://mathnet.opensourcedotnet.info
+//
+// Copyright (c) 2009 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 MathNet.Numerics.Distributions
+{
+    using System;
+    using System.Collections.Generic;
+    using Properties;
+
+    /// <summary>
+    /// This structure represents the type over which the <see cref="NormalGamma"/> distribution
+    /// is defined.
+    /// </summary>
+    public struct MeanPrecisionPair
+    {
+        private double mMean;
+        private double mPrecision;
+
+        /// <summary>
+        /// Constructs a new mean precision pair.
+        /// </summary>
+        /// <param name="m">The mean of the pair.</param>
+        /// <param name="p">The precision of the pair.</param>
+        public MeanPrecisionPair(double m, double p)
+        {
+            mMean = m;
+            mPrecision = p;
+        }
+
+        /// <summary>
+        /// Gets/sets the mean of the pair.
+        /// </summary>
+        public double Mean
+        {
+            get { return mMean; }
+            set { mMean = value; }
+        }
+
+        /// <summary>
+        /// Gets/sets the precision of the pair.
+        /// </summary>
+        public double Precision
+        {
+            get { return mPrecision; }
+            set { mPrecision = value; }
+        }
+    }
+
+    /// <summary>
+    /// <para>The <see cref="NormalGamma"/> distribution is the conjugate prior distribution for the <see cref="Normal"/>
+    /// distribution. It specifies a prior over the mean and precision of the <see cref="Normal"/> distribution.</para>
+    /// <para>It is parameterized by four numbers: the mean location, the mean scale, the precision shape and the
+    /// precision inverse scale.</para>
+    /// <para>The distribution NG(mu, tau | mloc,mscale,psscale,pinvscale) = Normal(mu | mloc, 1/(mscale*tau)) * Gamma(tau | psscale,pinvscale).</para>
+    /// <para>The following degenerate cases are special: when the precision is known,
+    /// the precision shape will encode the value of the precision while the precision inverse scale is positive
+    /// infinity. When the mean is known, the mean location will encode the value of the mean while the scale
+    /// will be positive infinity. A completely degenerate NormalGamma distribution with known mean and precision is possible as well.</para>
+    /// </summary>
+    /// <para>The distribution will use the <see cref="System.Random"/> by default. 
+    /// Users can get/set the random number generator by using the <see cref="RandomSource"/> property.</para>
+    /// <para>The statistics classes will check all the incoming parameters whether they are in the allowed
+    /// range. This might involve heavy computation. Optionally, by setting Control.CheckDistributionParameters
+    /// to false, all parameter checks can be turned off.</para></remarks>
+    public class NormalGamma
+    {
+        /// <summary>
+        /// The location of the mean.
+        /// </summary>
+        private double _meanLocation;
+
+        /// <summary>
+        /// The scale of the mean.
+        /// </summary>
+        private double _meanScale;
+
+        /// <summary>
+        /// The shape of the precision.
+        /// </summary>
+        private double _precisionShape;
+
+        /// <summary>
+        /// The inverse scale of the precision.
+        /// </summary>
+        private double _precisionInvScale;
+
+        /// <summary>
+        /// The distribution's random number generator.
+        /// </summary>
+        private Random _random;
+
+        /// <summary>
+        /// Constructs a NormalGamma distribution.
+        /// </summary>
+        /// <param name="meanLocation">The location of the mean.</param>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precShape">The shape of the precision.</param>
+        /// <param name="precInvScale">The inverse scale of the precision.</param>
+        public NormalGamma(double meanLocation, double meanScale, double precShape, double precInvScale)
+        {
+            SetParameters(meanLocation, meanScale, precShape, precInvScale);
+            _random = new Random();
+        }
+        
+        /// <summary>
+        /// Checks whether the parameters of the distribution are valid. 
+        /// </summary>
+        /// <param name="meanLocation">The location of the mean.</param>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precShape">The shape of the precision.</param>
+        /// <param name="precInvScale">The inverse scale of the precision.</param>
+        /// <returns>True when the parameters are valid, false otherwise.</returns>
+        private static bool IsValidParameterSet(double meanLocation, double meanScale, double precShape, double precInvScale)
+        {
+            if (meanScale <= 0.0 || precShape <= 0.0 || precInvScale <= 0.0 
+                || Double.IsNaN(meanLocation) || Double.IsNaN(meanScale) || Double.IsNaN(precShape) 
+                || Double.IsNaN(precInvScale))
+            {
+                return false;
+            }
+
+            return true;
+        }
+
+        /// <summary>
+        /// Sets the parameters of the distribution after checking their validity.
+        /// </summary>
+        /// <param name="meanLocation">The location of the mean.</param>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precShape">The shape of the precision.</param>
+        /// <param name="precInvScale">The inverse scale of the precision.</param>
+        /// <exception cref="ArgumentOutOfRangeException">When the parameters don't pass the <see cref="IsValidParameterSet"/> function.</exception>
+        private void SetParameters(double meanLocation, double meanScale, double precShape, double precInvScale)
+        {
+            if (Control.CheckDistributionParameters && !IsValidParameterSet(meanLocation, meanScale, precShape, precInvScale))
+            {
+                throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
+            }
+
+            _meanLocation = meanLocation;
+            _meanScale = meanScale;
+            _precisionShape = precShape;
+            _precisionInvScale = precInvScale;
+        }
+
+        /// <summary>
+        /// A string representation of the distribution.
+        /// </summary>
+        public override string ToString()
+        {
+            return "NormalGamma(Mean Location = " + _meanLocation + ", Mean Scale = " + _meanScale +
+                        ", Precision Shape = " + _precisionShape + ", Precision Inverse Scale = " + _precisionInvScale + ")";
+        }
+
+        /// <summary>
+        /// Gets the location of the mean.
+        /// </summary>
+        public double MeanLocation
+        {
+            get { return _meanLocation; }
+        }
+
+        /// <summary>
+        /// Gets the scale of the mean.
+        /// </summary>
+        public double MeanScale
+        {
+            get { return _meanScale; }
+        }
+
+        /// <summary>
+        /// Gets the shape of the precision.
+        /// </summary>
+        public double PrecisionShape
+        {
+            get { return _precisionShape; }
+        }
+
+        /// <summary>
+        /// Gets the inverse scale of the precision.
+        /// </summary>
+        public double PrecisionInverseScale
+        {
+            get { return _precisionInvScale; }
+        }
+
+        /// <summary>
+        /// Returns the marginal distribution for the mean of the <see cref="NormalGamma"/> distribution.
+        /// </summary>
+        /// <returns></returns>
+        public StudentT MeanMarginal()
+        {
+            return new StudentT(_meanLocation, _meanScale * _precisionShape / _precisionInvScale, 2.0 * _precisionShape);
+        }
+
+        /// <summary>
+        /// Returns the marginal distribution for the precision of the <see cref="NormalGamma"/> distribution.
+        /// </summary>
+        /// <returns></returns>
+        public Gamma PrecisionMarginal()
+        {
+            return new Gamma(_precisionShape, _precisionInvScale);
+        }
+
+        /*
+        /// <summary>
+        /// Gets the mean of the distribution.
+        /// </summary>
+        /// <value>The mean of the distribution.</value>
+        public MeanPrecisionPair Mean
+        {
+            get
+            {
+                if (Double.IsPositiveInfinity(_precisionInvScale))
+                {
+                    return new MeanPrecisionPair(_meanLocation, _precisionShape);
+                }
+                else
+                {
+                    return new MeanPrecisionPair(_meanLocation, _precisionShape / _precisionInvScale);
+                }
+            }
+        }
+
+        /// <summary>
+        /// Gets or sets the random number generator.
+        /// </summary>
+        /// <value>The random number generator used to generate a random sample.</value>
+        public System.Random RandomNumberGenerator { get; set; }
+
+        /// <summary>
+        /// The mode of the distribution.
+        /// </summary>
+        /// <value></value>
+        public MeanPrecisionPair Mode
+        {
+            get
+            {
+                if (Double.IsPositiveInfinity(_precisionInvScale))
+                {
+                    return new MeanPrecisionPair(_meanLocation, _precisionShape);
+                }
+                else
+                {
+                    return new MeanPrecisionPair(_meanLocation, _precisionShape / _precisionInvScale);
+                }
+            }
+        }
+
+        /// <summary>
+        /// The median of the distribution.
+        /// </summary>
+        /// <value></value>
+        public MeanPrecisionPair Median
+        {
+            get
+            {
+                if (Double.IsPositiveInfinity(_precisionInvScale))
+                {
+                    return new MeanPrecisionPair(_meanLocation, _precisionShape);
+                }
+                else
+                {
+                    return new MeanPrecisionPair(_meanLocation, _precisionShape / _precisionInvScale);
+                }
+            }
+        }
+
+        /// <summary>
+        /// Evaluates the probability density function for a NormalGamma distribution.
+        /// </summary>
+        public double Density(MeanPrecisionPair mp)
+        {
+            return Density(mp.Mean, mp.Precision);
+        }
+
+        /// <summary>
+        /// Evaluates the probability density function for a NormalGamma distribution.
+        /// </summary>
+        public double Density(double mean, double prec)
+        {
+            if (Double.IsPositiveInfinity(_precisionInvScale) && _meanScale == 0.0)
+            {
+                throw new NotImplementedException();
+            }
+            else if (Double.IsPositiveInfinity(_precisionInvScale))
+            {
+                throw new NotImplementedException();
+            }
+            else if (_meanScale == 0.0)
+            {
+                throw new NotImplementedException();
+            }
+            else
+            {
+                double e = -0.5 * prec * (mean - _meanLocation) * (mean - _meanLocation) - prec * _precisionInvScale;
+                return System.Math.Pow(prec * _precisionInvScale, _precisionShape) * System.Math.Exp(e)
+                        / (Math.Constants.Sqrt2Pi * System.Math.Sqrt(prec) * Math.SpecialFunctions.Gamma(_precisionShape));
+            }
+        }
+
+        /// <summary>
+        /// Evaluates the log probability density function for a NormalGamma distribution.
+        /// </summary>
+        public double DensityLn(MeanPrecisionPair mp)
+        {
+            return DensityLn(mp.Mean, mp.Precision);
+        }
+
+        /// <summary>
+        /// Evaluates the log probability density function for a NormalGamma distribution.
+        /// </summary>
+        public double DensityLn(double mean, double prec)
+        {
+            if (Double.IsPositiveInfinity(_precisionInvScale) && _meanScale == 0.0)
+            {
+                throw new NotImplementedException();
+            }
+            else if (Double.IsPositiveInfinity(_precisionInvScale))
+            {
+                throw new NotImplementedException();
+            }
+            else if (_meanScale == 0.0)
+            {
+                throw new NotImplementedException();
+            }
+            else
+            {
+                double e = -0.5 * prec * (mean - _meanLocation) * (mean - _meanLocation) - prec * _precisionInvScale;
+                return (_precisionShape - 0.5) * System.Math.Log(prec) + _precisionShape * System.Math.Log(_precisionInvScale) + e
+                        - Math.Constants.LogSqrt2Pi - Math.SpecialFunctions.GammaLn(_precisionShape);
+            }
+        }
+
+        /// <summary>
+        /// Samples a NormalGamma distributed random variable.
+        /// </summary>
+        /// <returns>A random number from this distribution.</returns>
+        public MeanPrecisionPair Sample()
+        {
+            return NormalGamma.Sample(RandomNumberGenerator, _meanLocation, _meanScale, _precisionShape, _precisionInvScale);
+        }
+
+        /// <summary>
+        /// Samples an array of NormalGamma distributed random variables.
+        /// </summary>
+        /// <param name="size">The number of variables needed.</param>
+        /// <returns>An array of random numbers from this distribution.</returns>
+        public MeanPrecisionPair[] Sample(int size)
+        {
+            return NormalGamma.Sample(RandomNumberGenerator, size, _meanLocation, _meanScale, _precisionShape, _precisionInvScale);
+        }
+
+        /// <summary>
+        /// Checks the parameters of a NormalGamma distribution.
+        /// </summary>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precShape">The shape of the precision.</param>
+        /// <param name="precInvScale">The inverse scale of the precision.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If the mean scale is negative.</exception>
+        /// <exception cref="ArgumentOutOfRangeException">If the inverse precision scale is negative.</exception>
+        /// <exception cref="ArgumentOutOfRangeException">If the precision shape is negative.</exception>
+        private static void CheckParameters(double meanScale, double precShape, double precInvScale)
+        {
+            if (meanScale < 0.0)
+            {
+                throw new ArgumentOutOfRangeException("meanScale", Resources.ParameterCannotBeNegative);
+            }
+            else if (precShape <= 0.0)
+            {
+                throw new ArgumentOutOfRangeException("precShape", Resources.ParameterCannotBeNegative);
+            }
+            else if (precInvScale <= 0.0)
+            {
+                throw new ArgumentOutOfRangeException("precInvScale", Resources.ParameterCannotBeNegative);
+            }
+        }
+
+        /// <summary>
+        /// Samples an array of NormalGamma distributed random variables.
+        /// </summary>
+        /// <param name="rnd">The random number generator to use.</param>
+        /// <param name="meanLocation">The location of the mean.</param>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precShape">The shape of the precision.</param>
+        /// <param name="precInvScale">The inverse scale of the precision.</param>
+        public static MeanPrecisionPair Sample(System.Random rnd, double meanLocation, double meanScale, double precShape, double precInvScale)
+        {
+            if (Control.CheckDistributionParameters)
+            {
+                CheckParameters(meanScale, precShape, precInvScale);
+            }
+
+            MeanPrecisionPair mp = new MeanPrecisionPair();
+
+            // Sample the precision.
+            if(Double.IsPositiveInfinity(precInvScale))
+            {
+                mp.Precision = precShape;
+            }
+            else
+            {
+                mp.Precision = Gamma.Sample(rnd, precShape, precInvScale);
+            }
+
+            // Sample the mean.
+            if (meanScale == 0.0)
+            {
+                mp.Mean = meanLocation;
+            }
+            else
+            {
+                mp.Mean = Normal.Sample(rnd, meanLocation, System.Math.Sqrt(meanScale / mp.Precision));
+            }
+
+            return mp;
+        }
+
+        /// <summary>
+        /// Samples an array of NormalGamma distributed random variables.
+        /// </summary>
+        /// <param name="rnd">The random number generator to use.</param>
+        /// <param name="n">The number of variables needed.</param>
+        /// <param name="meanLocation">The location of the mean.</param>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precShape">The shape of the precision.</param>
+        /// <param name="precInvScale">The inverse scale of the precision.</param>
+        public static MeanPrecisionPair[] Sample(System.Random rnd, int n, double meanLocation, double meanScale, double precShape, double precInvScale)
+        {
+            if (Control.CheckDistributionParameters)
+            {
+                CheckParameters(meanScale, precShape, precInvScale);
+            }
+
+            // First sample all the precisions independently.
+            double[] precs = null;
+            if (Double.IsPositiveInfinity(precInvScale))
+            {
+                precs = new double[n];
+                for (int i = 0; i < n; i++)
+                {
+                    precs[i] = precShape;
+                }
+            }
+            else
+            {
+                precs = Gamma.Sample(rnd, n, precShape, precInvScale);
+            }
+
+            // Construct all the mean precision pairs.
+            MeanPrecisionPair[] arr = new MeanPrecisionPair[n];
+
+            // Conditionally sample all the mean.
+            for (int i = 0; i < n; i++)
+            {
+                arr[i].Precision = precs[i];
+                if (meanScale == 0.0)
+                {
+                    arr[i].Mean = meanLocation;
+                }
+                else
+                {
+                    arr[i].Mean = Normal.Sample(rnd, meanLocation, System.Math.Sqrt(meanScale / precs[i]));
+                }
+            }
+
+            return arr;
+        }*/
+    }
+}
\ No newline at end of file
diff --git a/src/Silverlight/Silverlight.csproj b/src/Silverlight/Silverlight.csproj
index 068a9b4c..292be4ad 100644
--- a/src/Silverlight/Silverlight.csproj
+++ b/src/Silverlight/Silverlight.csproj
@@ -128,6 +128,9 @@
     <Compile Include="..\Numerics\Distributions\Multivariate\Multinomial.cs">
       <Link>Distributions\Multivariate\Multinomial.cs</Link>
     </Compile>
+    <Compile Include="..\Numerics\Distributions\Multivariate\NormalGamma.cs">
+      <Link>Distributions\Multivariate\NormalGamma.cs</Link>
+    </Compile>
     <Compile Include="..\Numerics\GlobalizationHelper.cs">
       <Link>GlobalizationHelper.cs</Link>
     </Compile>
diff --git a/src/UnitTests/DistributionTests/Continuous/StudentTTests.cs b/src/UnitTests/DistributionTests/Continuous/StudentTTests.cs
index e6571239..c6a0ed3d 100644
--- a/src/UnitTests/DistributionTests/Continuous/StudentTTests.cs
+++ b/src/UnitTests/DistributionTests/Continuous/StudentTTests.cs
@@ -51,21 +51,25 @@ namespace MathNet.Numerics.UnitTests.DistributionTests
             AssertEx.AreEqual<double>(1.0, n.DegreesOfFreedom);
         }
 
-        /*[Test, MultipleAsserts]
-        [Row(0.0, 0.0)]
-        [Row(0.0, 0.1)]
-        [Row(0.0, 1.0)]
-        [Row(0.0, 10.0)]
-        [Row(10.0, 1.0)]
-        [Row(-5.0, 100.0)]
-        [Row(0.0, Double.PositiveInfinity)]
-        public void CanCreateNormal(double mean, double sdev)
+        [Test, MultipleAsserts]
+        [Row(0.0, 1.0, 1.0)]
+        [Row(0.0, 0.1, 1.0)]
+        [Row(0.0, 1.0, 1.0)]
+        [Row(0.0, 10.0, 1.0)]
+        [Row(0.0, 10.0, Double.PositiveInfinity)]
+        [Row(10.0, 1.0, 1.0)]
+        [Row(-5.0, 100.0, 1.0)]
+        [Row(0.0, Double.PositiveInfinity, 1.0)]
+        public void CanCreateStudentT(double location, double scale, double dof)
         {
-            var n = new Normal(mean, sdev);
-            AssertEx.AreEqual<double>(mean, n.Mean);
-            AssertEx.AreEqual<double>(sdev, n.StdDev);
+            var n = new StudentT(location, scale, dof);
+            AssertEx.AreEqual<double>(0.0, n.Location);
+            AssertEx.AreEqual<double>(1.0, n.Scale);
+            AssertEx.AreEqual<double>(1.0, n.DegreesOfFreedom);
         }
 
+        /*
+
         [Test]
         [ExpectedException(typeof(ArgumentOutOfRangeException))]
         [Row(Double.NaN, 1.0)]
diff --git a/src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs b/src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs
new file mode 100644
index 00000000..84ffbed9
--- /dev/null
+++ b/src/UnitTests/DistributionTests/Multivariate/NormalGammaTests.cs
@@ -0,0 +1,60 @@
+// <copyright file="NormalGammaTests.cs" company="Math.NET">
+// Math.NET Numerics, part of the Math.NET Project
+// http://mathnet.opensourcedotnet.info
+//
+// Copyright (c) 2009 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 MathNet.Numerics.UnitTests.DistributionTests
+{
+    using System;
+    using System.Linq;
+    using MbUnit.Framework;
+    using MathNet.Numerics.Distributions;
+
+    [TestFixture]
+    public class NormalGammaTests
+    {
+        [Test, MultipleAsserts]
+        public void NormalGammaTest()
+        {
+            NormalGamma ng = new NormalGamma(10.0, 1.0, 2.0, 2.0);
+
+            AssertEx.AreEqual<double>(10.0, ng.MeanLocation);
+            AssertEx.AreEqual<double>(1.0, ng.MeanScale);
+            AssertEx.AreEqual<double>(2.0, ng.PrecisionShape);
+            AssertEx.AreEqual<double>(2.0, ng.PrecisionInverseScale);
+        }
+
+        [Test]
+        [Row(1.0, -1.3, 2.0, 2.0)]
+        [Row(1.0, 1.0, -1.0, 1.0)]
+        [Row(1.0, 1.0, 1.0, -1.0)]
+        [ExpectedException(typeof(ArgumentOutOfRangeException))]
+        public void InvalidParams(double a, double b, double c, double d)
+        {
+            var nb = new NormalGamma(a, b, c, d);
+        }
+    }
+}
\ No newline at end of file