Distributions: ctor overloads that accept Random argument

src/Numerics/Distributions/Continuous/Beta.cs

@@ -71,8 +71,21 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="ArgumentOutOfRangeException">If any of the Beta parameters are negative.</exception>
         public Beta(double a, double b)
         {
+            _random = new Random();
+            SetParameters(a, b);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Beta class.
+        /// </summary>
+        /// <param name="a">The a shape parameter of the Beta distribution.</param>
+        /// <param name="b">The b shape parameter of the Beta distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If any of the Beta parameters are negative.</exception>
+        public Beta(double a, double b, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(a, b);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -143,7 +156,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Cauchy.cs

@@ -54,27 +54,33 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Cauchy"/> class with the location parameter set to 0 and the scale parameter set to 1
         /// </summary>
-        public Cauchy()
-            : this(0, 1)
+        public Cauchy() : this(0, 1)
         {
         }
 
         /// <summary>
         /// Initializes a new instance of the <see cref="Cauchy"/> class. 
         /// </summary>
-        /// <param name="location">
-        /// The location parameter for the distribution.
-        /// </param>
-        /// <param name="scale">
-        /// The scale parameter for the distribution.
-        /// </param>
-        /// <exception cref="ArgumentException">
-        /// If <paramref name="scale"/> is negative.
-        /// </exception>
+        /// <param name="location">The location parameter for the distribution.</param>
+        /// <param name="scale">The scale parameter for the distribution.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> is negative.</exception>
         public Cauchy(double location, double scale)
         {
+            _random = new Random();
+            SetParameters(location, scale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Cauchy"/> class. 
+        /// </summary>
+        /// <param name="location">The location parameter for the distribution.</param>
+        /// <param name="scale">The scale parameter for the distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> is negative.</exception>
+        public Cauchy(double location, double scale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(location, scale);
-            RandomSource = new Random();
         }
 
         /// <summary>

src/Numerics/Distributions/Continuous/Chi.cs

@@ -57,13 +57,22 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Chi"/> class. 
         /// </summary>
-        /// <param name="dof">
-        /// The degrees of freedom for the Chi distribution.
-        /// </param>
+        /// <param name="dof">The degrees of freedom for the Chi distribution.</param>
         public Chi(double dof)
         {
+            _random = new Random();
+            SetParameters(dof);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Chi"/> class. 
+        /// </summary>
+        /// <param name="dof">The degrees of freedom for the Chi distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Chi(double dof, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(dof);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -123,7 +132,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/ChiSquare.cs

@@ -54,13 +54,22 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="ChiSquare"/> class. 
         /// </summary>
-        /// <param name="dof">
-        /// The degrees of freedom for the ChiSquare distribution.
-        /// </param>
+        /// <param name="dof">The degrees of freedom for the ChiSquare distribution.</param>
         public ChiSquare(double dof)
         {
+            _random = new Random();
+            SetParameters(dof);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="ChiSquare"/> class. 
+        /// </summary>
+        /// <param name="dof">The degrees of freedom for the ChiSquare distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public ChiSquare(double dof, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(dof);
-            RandomSource = new Random();
         }
 
         /// <summary>

src/Numerics/Distributions/Continuous/ContinuousUniform.cs

@@ -63,8 +63,7 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the ContinuousUniform class with lower bound 0 and upper bound 1.
         /// </summary>
-        public ContinuousUniform()
-            : this(0.0, 1.0)
+        public ContinuousUniform() : this(0.0, 1.0)
         {
         }
 
@@ -76,8 +75,21 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="ArgumentException">If the upper bound is smaller than the lower bound.</exception>
         public ContinuousUniform(double lower, double upper)
         {
+            _random = new Random();
+            SetParameters(lower, upper);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the ContinuousUniform class with given lower and upper bounds.
+        /// </summary>
+        /// <param name="lower">Lower bound.</param>
+        /// <param name="upper">Upper bound; must be at least as large as lower.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentException">If the upper bound is smaller than the lower bound.</exception>
+        public ContinuousUniform(double lower, double upper, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(lower, upper);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -155,7 +167,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Erlang.cs

@@ -61,16 +61,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Erlang"/> class. 
         /// </summary>
-        /// <param name="shape">
-        /// The shape of the Erlang distribution.
-        /// </param>
-        /// <param name="invScale">
-        /// The inverse scale of the Erlang distribution.
-        /// </param>
+        /// <param name="shape">The shape of the Erlang distribution.</param>
+        /// <param name="invScale">The inverse scale of the Erlang distribution.</param>
         public Erlang(int shape, double invScale)
         {
+            _random = new Random();
+            SetParameters(shape, invScale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Erlang"/> class. 
+        /// </summary>
+        /// <param name="shape">The shape of the Erlang distribution.</param>
+        /// <param name="invScale">The inverse scale of the Erlang distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Erlang(int shape, double invScale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(shape, invScale);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -186,7 +194,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Exponential.cs

@@ -54,13 +54,22 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Exponential"/> class.
         /// </summary>
-        /// <param name="lambda">
-        /// The lambda parameter of the Exponential distribution.
-        /// </param>
+        /// <param name="lambda">The lambda parameter of the Exponential distribution.</param>
         public Exponential(double lambda)
         {
+            _random = new Random();
+            SetParameters(lambda);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Exponential"/> class.
+        /// </summary>
+        /// <param name="lambda">The lambda parameter of the Exponential distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Exponential(double lambda, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(lambda);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -125,7 +134,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/FisherSnedecor.cs

@@ -59,16 +59,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="FisherSnedecor"/> class. 
         /// </summary>
-        /// <param name="d1">
-        /// The first parameter - degree of freedom.
-        /// </param>
-        /// <param name="d2">
-        /// The second parameter - degree of freedom.
-        /// </param>
+        /// <param name="d1">The first parameter - degree of freedom.</param>
+        /// <param name="d2">The second parameter - degree of freedom.</param>
         public FisherSnedecor(double d1, double d2)
         {
+            _random = new Random();
+            SetParameters(d1, d2);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="FisherSnedecor"/> class. 
+        /// </summary>
+        /// <param name="d1">The first parameter - degree of freedom.</param>
+        /// <param name="d2">The second parameter - degree of freedom.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public FisherSnedecor(double d1, double d2, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(d1, d2);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -145,12 +153,11 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)
                 {
-                    throw new ArgumentNullException(Resources.InvalidDistributionParameters);
+                    throw new ArgumentNullException();
                 }
 
                 _random = value;

src/Numerics/Distributions/Continuous/Gamma.cs

@@ -71,8 +71,20 @@ namespace MathNet.Numerics.Distributions
         /// <param name="invScale">The inverse scale of the Gamma distribution.</param>
         public Gamma(double shape, double invScale)
         {
+            _random = new Random();
+            SetParameters(shape, invScale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Gamma class.
+        /// </summary>
+        /// <param name="shape">The shape of the Gamma distribution.</param>
+        /// <param name="invScale">The inverse scale of the Gamma distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Gamma(double shape, double invScale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(shape, invScale);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -189,7 +201,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/InverseGamma.cs

@@ -60,16 +60,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="InverseGamma"/> class. 
         /// </summary>
-        /// <param name="shape">
-        /// The shape (alpha) parameter of the inverse Gamma distribution.
-        /// </param>
-        /// <param name="scale">
-        /// The scale (beta) parameter of the inverse Gamma distribution.
-        /// </param>
+        /// <param name="shape">The shape (alpha) parameter of the inverse Gamma distribution.</param>
+        /// <param name="scale">The scale (beta) parameter of the inverse Gamma distribution.</param>
         public InverseGamma(double shape, double scale)
         {
-            SetParameters(shape, scale);
             _random = new Random();
+            SetParameters(shape, scale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="InverseGamma"/> class. 
+        /// </summary>
+        /// <param name="shape">The shape (alpha) parameter of the inverse Gamma distribution.</param>
+        /// <param name="scale">The scale (beta) parameter of the inverse Gamma distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public InverseGamma(double shape, double scale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
+            SetParameters(shape, scale);
         }
 
         /// <summary>
@@ -155,7 +163,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Laplace.cs

@@ -76,27 +76,33 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Laplace"/> class (location = 0, scale = 1). 
         /// </summary>
-        public Laplace()
-            : this(0.0, 1.0)
+        public Laplace() : this(0.0, 1.0)
         {
         }
 
         /// <summary>
         /// Initializes a new instance of the <see cref="Laplace"/> class. 
         /// </summary>
-        /// <param name="location">
-        /// The location for the Laplace distribution.
-        /// </param>
-        /// <param name="scale">
-        /// The scale for the Laplace distribution.
-        /// </param>
-        /// <exception cref="ArgumentException">
-        /// If <paramref name="scale"/> is negative.
-        /// </exception>
+        /// <param name="location">The location for the Laplace distribution.</param>
+        /// <param name="scale">The scale for the Laplace distribution.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> is negative.</exception>
         public Laplace(double location, double scale)
         {
+            _random = new Random();
+            SetParameters(location, scale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Laplace"/> class. 
+        /// </summary>
+        /// <param name="location">The location for the Laplace distribution.</param>
+        /// <param name="scale">The scale for the Laplace distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> is negative.</exception>
+        public Laplace(double location, double scale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(location, scale);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -154,7 +160,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/LogNormal.cs

@@ -61,16 +61,26 @@ namespace MathNet.Numerics.Distributions
         /// The distribution will be initialized with the default <seealso cref="System.Random"/>
         /// random number generator.
         /// </summary>
-        /// <param name="mu">
-        /// The mu of the logarithm of the distribution.
-        /// </param>
-        /// <param name="sigma">
-        /// The standard deviation of the logarithm of the distribution.
-        /// </param>
+        /// <param name="mu">The mu of the logarithm of the distribution.</param>
+        /// <param name="sigma">The standard deviation of the logarithm of the distribution.</param>
         public LogNormal(double mu, double sigma)
         {
+            _random = new Random();
+            SetParameters(mu, sigma);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="LogNormal"/> class. 
+        /// The distribution will be initialized with the default <seealso cref="System.Random"/>
+        /// random number generator.
+        /// </summary>
+        /// <param name="mu">The mu of the logarithm of the distribution.</param>
+        /// <param name="sigma">The standard deviation of the logarithm of the distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public LogNormal(double mu, double sigma, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(mu, sigma);
-            RandomSource = new Random();
         }
 
         /// <summary>

src/Numerics/Distributions/Continuous/Normal.cs

@@ -61,8 +61,17 @@ namespace MathNet.Numerics.Distributions
         /// and standard deviation 1.0. The distribution will
         /// be initialized with the default <seealso cref="System.Random"/> random number generator.
         /// </summary>
-        public Normal()
-            : this(0.0, 1.0)
+        public Normal() : this(0.0, 1.0)
+        {
+        }
+        
+        /// <summary>
+        /// Initializes a new instance of the Normal class. This is a normal distribution with mean 0.0
+        /// and standard deviation 1.0. The distribution will
+        /// be initialized with the default <seealso cref="System.Random"/> random number generator.
+        /// </summary>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Normal(Random randomSource) : this(0.0, 1.0, randomSource)
         {
         }
 
@@ -74,8 +83,21 @@ namespace MathNet.Numerics.Distributions
         /// <param name="stddev">The standard deviation of the normal distribution.</param>
         public Normal(double mean, double stddev)
         {
+            _random = new Random();
+            SetParameters(mean, stddev);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Normal class with a particular mean and standard deviation. The distribution will
+        /// be initialized with the default <seealso cref="System.Random"/> random number generator.
+        /// </summary>
+        /// <param name="mean">The mean of the normal distribution.</param>
+        /// <param name="stddev">The standard deviation of the normal distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Normal(double mean, double stddev, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(mean, stddev);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -185,7 +207,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Pareto.cs

@@ -61,19 +61,26 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Pareto"/> class. 
         /// </summary>
-        /// <param name="scale">
-        /// The scale parameter of the distribution.
-        /// </param>
-        /// <param name="shape">
-        /// The shape parameter of the distribution.
-        /// </param>
-        /// <exception cref="ArgumentException">
-        /// If <paramref name="scale"/> or <paramref name="shape"/> are negative.
-        /// </exception>
+        /// <param name="scale">The scale parameter of the distribution.</param>
+        /// <param name="shape">The shape parameter of the distribution.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> or <paramref name="shape"/> are negative.</exception>
         public Pareto(double scale, double shape)
         {
+            _random = new Random();
+            SetParameters(scale, shape);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Pareto"/> class. 
+        /// </summary>
+        /// <param name="scale">The scale parameter of the distribution.</param>
+        /// <param name="shape">The shape parameter of the distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> or <paramref name="shape"/> are negative.</exception>
+        public Pareto(double scale, double shape, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(scale, shape);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -151,7 +158,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Rayleigh.cs

@@ -57,16 +57,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Rayleigh"/> class. 
         /// </summary>
-        /// <param name="scale">
-        /// The scale parameter of the distribution.
-        /// </param>
-        /// <exception cref="ArgumentException">
-        /// If <paramref name="scale"/> is negative.
-        /// </exception>
+        /// <param name="scale">The scale parameter of the distribution.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> is negative.</exception>
         public Rayleigh(double scale)
         {
+            _random = new Random();
+            SetParameters(scale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Rayleigh"/> class. 
+        /// </summary>
+        /// <param name="scale">The scale parameter of the distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentException">If <paramref name="scale"/> is negative.</exception>
+        public Rayleigh(double scale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(scale);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -131,7 +139,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Stable.cs

@@ -72,22 +72,28 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Stable"/> class. 
         /// </summary>
-        /// <param name="alpha">
-        /// The stability parameter of the distribution.
-        /// </param>
-        /// <param name="beta">
-        /// The skewness parameter of the distribution.
-        /// </param>
-        /// <param name="scale">
-        /// The scale parameter of the distribution.
-        /// </param>
-        /// <param name="location">
-        /// The location parameter of the distribution.
-        /// </param>
+        /// <param name="alpha">The stability parameter of the distribution.</param>
+        /// <param name="beta">The skewness parameter of the distribution.</param>
+        /// <param name="scale">The scale parameter of the distribution.</param>
+        /// <param name="location">The location parameter of the distribution.</param>
         public Stable(double alpha, double beta, double scale, double location)
         {
+            _random = new Random();
+            SetParameters(alpha, beta, scale, location);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Stable"/> class. 
+        /// </summary>
+        /// <param name="alpha">The stability parameter of the distribution.</param>
+        /// <param name="beta">The skewness parameter of the distribution.</param>
+        /// <param name="scale">The scale parameter of the distribution.</param>
+        /// <param name="location">The location parameter of the distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Stable(double alpha, double beta, double scale, double location, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(alpha, beta, scale, location);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -200,7 +206,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/StudentT.cs

@@ -77,8 +77,7 @@ namespace MathNet.Numerics.Distributions
         /// scale 1.0 and degrees of freedom 1. The distribution will
         /// be initialized with the default <seealso cref="System.Random"/> random number generator.
         /// </summary>
-        public StudentT()
-            : this(0.0, 1.0, 1.0)
+        public StudentT() : this(0.0, 1.0, 1.0)
         {
         }
 
@@ -92,8 +91,23 @@ namespace MathNet.Numerics.Distributions
         /// <param name="dof">The degrees of freedom for the Student t-distribution.</param>
         public StudentT(double location, double scale, double dof)
         {
+            _random = new Random();
+            SetParameters(location, scale, dof);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the StudentT class with a particular location, scale and degrees of
+        /// freedom. The distribution will
+        /// be initialized with the default <seealso cref="System.Random"/> random number generator.
+        /// </summary>
+        /// <param name="location">The location of the Student t-distribution.</param>
+        /// <param name="scale">The scale of the Student t-distribution.</param>
+        /// <param name="dof">The degrees of freedom for the Student t-distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public StudentT(double location, double scale, double dof, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(location, scale, dof);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -179,7 +193,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Continuous/Weibull.cs

@@ -78,8 +78,20 @@ namespace MathNet.Numerics.Distributions
         /// <param name="scale">The inverse scale of the Weibull distribution.</param>
         public Weibull(double shape, double scale)
         {
+            _random = new Random();
+            SetParameters(shape, scale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Weibull class.
+        /// </summary>
+        /// <param name="shape">The shape of the Weibull distribution.</param>
+        /// <param name="scale">The inverse scale of the Weibull distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Weibull(double shape, double scale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(shape, scale);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -153,7 +165,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/Bernoulli.cs

@@ -59,8 +59,20 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="ArgumentOutOfRangeException">If the Bernoulli parameter is not in the range [0,1].</exception>
         public Bernoulli(double p)
         {
+            _random = new Random();
+            SetParameters(p);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Bernoulli class.
+        /// </summary>
+        /// <param name="p">The probability of generating one.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If the Bernoulli parameter is not in the range [0,1].</exception>
+        public Bernoulli(double p, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(p);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -120,7 +132,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/Binomial.cs

@@ -66,8 +66,22 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
         public Binomial(double p, int n)
         {
+            _random = new Random();
+            SetParameters(p, n);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Binomial class.
+        /// </summary>
+        /// <param name="p">The success probability of a trial.</param>
+        /// <param name="n">The number of trials.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If <paramref name="p"/> is not in the interval [0.0,1.0].</exception>
+        /// <exception cref="ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
+        public Binomial(double p, int n, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(p, n);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -146,7 +160,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/Categorical.cs

@@ -63,8 +63,21 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="ArgumentException">If any of the probabilities are negative or do not sum to one.</exception>
         public Categorical(double[] probabilityMass)
         {
+            _random = new Random();
+            SetParameters(probabilityMass);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Categorical class.
+        /// </summary>
+        /// <param name="probabilityMass">An array of nonnegative ratios: this array does not need to be normalized 
+        /// as this is often impossible using floating point arithmetic.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentException">If any of the probabilities are negative or do not sum to one.</exception>
+        public Categorical(double[] probabilityMass, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(probabilityMass);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -89,8 +102,8 @@ namespace MathNet.Numerics.Distributions
                 p[i] = histogram[i].Count;
             }
 
+            _random = new Random();
             SetParameters(p);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -196,7 +209,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/ConwayMaxwellPoisson.cs

@@ -87,16 +87,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="ConwayMaxwellPoisson"/> class. 
         /// </summary>
-        /// <param name="lambda">
-        /// The lambda parameter.
-        /// </param>
-        /// <param name="nu">
-        /// The nu parameter.
-        /// </param>
+        /// <param name="lambda">The lambda parameter.</param>
+        /// <param name="nu">The nu parameter.</param>
         public ConwayMaxwellPoisson(double lambda, double nu)
         {
+            _random = new Random();
+            SetParameters(lambda, nu);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="ConwayMaxwellPoisson"/> class. 
+        /// </summary>
+        /// <param name="lambda">The lambda parameter.</param>
+        /// <param name="nu">The nu parameter.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public ConwayMaxwellPoisson(double lambda, double nu, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(lambda, nu);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -178,7 +186,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/DiscreteUniform.cs

@@ -64,8 +64,20 @@ namespace MathNet.Numerics.Distributions
         /// <param name="upper">Upper bound; must be at least as large as <paramref name="lower"/>.</param>
         public DiscreteUniform(int lower, int upper)
         {
+            _random = new Random();
+            SetParameters(lower, upper);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the DiscreteUniform class.
+        /// </summary>
+        /// <param name="lower">Lower bound.</param>
+        /// <param name="upper">Upper bound; must be at least as large as <paramref name="lower"/>.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public DiscreteUniform(int lower, int upper, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(lower, upper);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -140,7 +152,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/Geometric.cs

@@ -59,8 +59,20 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="ArgumentOutOfRangeException">If the Geometric parameter is not in the range [0,1].</exception>
         public Geometric(double p)
         {
+            _random = new Random();
+            SetParameters(p);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Geometric class.
+        /// </summary>
+        /// <param name="p">The probability of generating one.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If the Geometric parameter is not in the range [0,1].</exception>
+        public Geometric(double p, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(p);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -122,7 +134,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/Hypergeometric.cs

@@ -77,8 +77,21 @@ namespace MathNet.Numerics.Distributions
         /// <param name="n">The n parameter of the distribution.</param>
         public Hypergeometric(int populationSize, int m, int n)
         {
+            _random = new Random();
+            SetParameters(populationSize, m, n);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Hypergeometric class.
+        /// </summary>
+        /// <param name="populationSize">The population size.</param>
+        /// <param name="m">The m parameter of the distribution.</param>
+        /// <param name="n">The n parameter of the distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Hypergeometric(int populationSize, int m, int n, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(populationSize, m, n);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -167,7 +180,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/NegativeBinomial.cs

@@ -64,7 +64,6 @@ namespace MathNet.Numerics.Distributions
         public double R
         {
             get { return _r; }
-
             set { SetParameters(value, _p); }
         }
 
@@ -74,7 +73,6 @@ namespace MathNet.Numerics.Distributions
         public double P
         {
             get { return _p; }
-
             set { SetParameters(_r, value); }
         }
 
@@ -85,8 +83,20 @@ namespace MathNet.Numerics.Distributions
         /// <param name="p">The probability of a trial resulting in success.</param>
         public NegativeBinomial(double r, double p)
         {
+            _random = new Random();
+            SetParameters(r, p);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="NegativeBinomial"/> class. 
+        /// </summary>
+        /// <param name="r">The number of trials.</param>
+        /// <param name="p">The probability of a trial resulting in success.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public NegativeBinomial(double r, double p, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(r, p);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -146,7 +156,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/Poisson.cs

@@ -56,7 +56,6 @@ namespace MathNet.Numerics.Distributions
         public double Lambda
         {
             get { return _lambda; }
-
             set { SetParameters(value); }
         }
 
@@ -67,8 +66,20 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="System.ArgumentOutOfRangeException">If <paramref name="lambda"/> is equal or less then 0.0.</exception>
         public Poisson(double lambda)
         {
+            _random = new Random();
+            SetParameters(lambda);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Poisson"/> class.
+        /// </summary>
+        /// <param name="lambda">The Poisson distribution parameter λ.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="System.ArgumentOutOfRangeException">If <paramref name="lambda"/> is equal or less then 0.0.</exception>
+        public Poisson(double lambda, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(lambda);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -115,7 +126,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Discrete/Zipf.cs

@@ -62,16 +62,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Zipf"/> class. 
         /// </summary>
-        /// <param name="s">
-        /// The s parameter of the distribution.
-        /// </param>
-        /// <param name="n">
-        /// The n parameter of the distribution.
-        /// </param>
+        /// <param name="s">The s parameter of the distribution.</param>
+        /// <param name="n">The n parameter of the distribution.</param>
         public Zipf(double s, int n)
         {
+            _random = new Random();
+            SetParameters(s, n);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Zipf"/> class. 
+        /// </summary>
+        /// <param name="s">The s parameter of the distribution.</param>
+        /// <param name="n">The n parameter of the distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Zipf(double s, int n, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(s, n);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -143,7 +151,6 @@ namespace MathNet.Numerics.Distributions
         public Random RandomSource
         {
             get { return _random; }
-
             set
             {
                 if (value == null)

src/Numerics/Distributions/Multivariate/Dirichlet.cs

@@ -62,21 +62,27 @@ namespace MathNet.Numerics.Distributions
         /// <param name="alpha">An array with the Dirichlet parameters.</param>
         public Dirichlet(double[] alpha)
         {
+            _random = new Random();
             SetParameters(alpha);
-            RandomSource = new Random();
         }
 
         /// <summary>
-        /// Initializes a new instance of the <see cref="Dirichlet"/> class. 
-        /// <seealso cref="System.Random"/>
-        /// random number generator.
+        /// Initializes a new instance of the Dirichlet class. The distribution will
+        /// be initialized with the default <seealso cref="System.Random"/> random number generator.
         /// </summary>
-        /// <param name="alpha">
-        /// The value of each parameter of the Dirichlet distribution.
-        /// </param>
-        /// <param name="k">
-        /// The dimension of the Dirichlet distribution.
-        /// </param>
+        /// <param name="alpha">An array with the Dirichlet parameters.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Dirichlet(double[] alpha, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
+            SetParameters(alpha);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Dirichlet"/> class. 
+        /// <seealso cref="System.Random"/>random number generator.</summary>
+        /// <param name="alpha">The value of each parameter of the Dirichlet distribution.</param>
+        /// <param name="k">The dimension of the Dirichlet distribution.</param>
         public Dirichlet(double alpha, int k)
         {
             // Create a parameter structure.
@@ -86,8 +92,27 @@ namespace MathNet.Numerics.Distributions
                 parm[i] = alpha;
             }
 
+            _random = new Random();
+            SetParameters(parm);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Dirichlet"/> class. 
+        /// <seealso cref="System.Random"/>random number generator.</summary>
+        /// <param name="alpha">The value of each parameter of the Dirichlet distribution.</param>
+        /// <param name="k">The dimension of the Dirichlet distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Dirichlet(double alpha, int k, Random randomSource)
+        {
+            // Create a parameter structure.
+            var parm = new double[k];
+            for (var i = 0; i < k; i++)
+            {
+                parm[i] = alpha;
+            }
+
+            _random = randomSource ?? new Random();
             SetParameters(parm);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -301,11 +326,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         public Random RandomSource
         {
-            get
-            {
-                return _random;
-            }
-
+            get { return _random; }
             set
             {
                 if (value == null)

src/Numerics/Distributions/Multivariate/InverseWishart.cs

@@ -67,16 +67,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="InverseWishart"/> class. 
         /// </summary>
-        /// <param name="nu">
-        /// The degrees of freedom for the inverse Wishart distribution.
-        /// </param>
-        /// <param name="s">
-        /// The scale matrix for the inverse Wishart distribution.
-        /// </param>
+        /// <param name="nu">The degrees of freedom for the inverse Wishart distribution.</param>
+        /// <param name="s">The scale matrix for the inverse Wishart distribution.</param>
         public InverseWishart(double nu, Matrix<double> s)
         {
+            _random = new Random();
+            SetParameters(nu, s);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="InverseWishart"/> class. 
+        /// </summary>
+        /// <param name="nu">The degrees of freedom for the inverse Wishart distribution.</param>
+        /// <param name="s">The scale matrix for the inverse Wishart distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public InverseWishart(double nu, Matrix<double> s, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(nu, s);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -172,11 +180,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         public Random RandomSource
         {
-            get
-            {
-                return _random;
-            }
-
+            get { return _random; }
             set
             {
                 if (value == null)

src/Numerics/Distributions/Multivariate/MatrixNormal.cs

@@ -68,22 +68,28 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="MatrixNormal"/> class. 
         /// </summary>
-        /// <param name="m">
-        /// The mean of the matrix normal.
-        /// </param>
-        /// <param name="v">
-        /// The covariance matrix for the rows.
-        /// </param>
-        /// <param name="k">
-        /// The covariance matrix for the columns.
-        /// </param>
-        /// <exception cref="ArgumentOutOfRangeException">
-        /// If the dimensions of the mean and two covariance matrices don't match.
-        /// </exception>
+        /// <param name="m">The mean of the matrix normal.</param>
+        /// <param name="v">The covariance matrix for the rows.</param>
+        /// <param name="k">The covariance matrix for the columns.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If the dimensions of the mean and two covariance matrices don't match.</exception>
         public MatrixNormal(Matrix<double> m, Matrix<double> v, Matrix<double> k)
         {
+            _random = new Random();
+            SetParameters(m, v, k);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="MatrixNormal"/> class. 
+        /// </summary>
+        /// <param name="m">The mean of the matrix normal.</param>
+        /// <param name="v">The covariance matrix for the rows.</param>
+        /// <param name="k">The covariance matrix for the columns.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If the dimensions of the mean and two covariance matrices don't match.</exception>
+        public MatrixNormal(Matrix<double> m, Matrix<double> v, Matrix<double> k, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(m, v, k);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -212,11 +218,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         public Random RandomSource
         {
-            get
-            {
-                return _random;
-            }
-
+            get { return _random; }
             set
             {
                 if (value == null)

src/Numerics/Distributions/Multivariate/Multinomial.cs

@@ -73,8 +73,23 @@ namespace MathNet.Numerics.Distributions
         /// <exception cref="ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
         public Multinomial(double[] p, int n)
         {
+            _random = new Random();
+            SetParameters(p, n);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the Multinomial class.
+        /// </summary>
+        /// <param name="p">An array of nonnegative ratios: this array does not need to be normalized 
+        /// as this is often impossible using floating point arithmetic.</param>
+        /// <param name="n">The number of trials.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <exception cref="ArgumentOutOfRangeException">If any of the probabilities are negative or do not sum to one.</exception>
+        /// <exception cref="ArgumentOutOfRangeException">If <paramref name="n"/> is negative.</exception>
+        public Multinomial(double[] p, int n, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(p, n);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -198,11 +213,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         public Random RandomSource
         {
-            get
-            {
-                return _random;
-            }
-
+            get { return _random; }
             set
             {
                 if (value == null)

src/Numerics/Distributions/Multivariate/NormalGamma.cs

@@ -49,12 +49,8 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="MeanPrecisionPair"/> struct. 
         /// </summary>
-        /// <param name="m">
-        /// The mean of the pair.
-        /// </param>
-        /// <param name="p">
-        /// The precision of the pair.
-        /// </param>
+        /// <param name="m">The mean of the pair.</param>
+        /// <param name="p">The precision of the pair.</param>
         public MeanPrecisionPair(double m, double p)
         {
             _mean = m;
@@ -141,22 +137,28 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="NormalGamma"/> class. 
         /// </summary>
-        /// <param name="meanLocation">
-        /// The location of the mean.
-        /// </param>
-        /// <param name="meanScale">
-        /// The scale of the mean.
-        /// </param>
-        /// <param name="precisionShape">
-        /// The shape of the precision.
-        /// </param>
-        /// <param name="precisionInverseScale">
-        /// The inverse scale of the precision.
-        /// </param>
+        /// <param name="meanLocation">The location of the mean.</param>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precisionShape">The shape of the precision.</param>
+        /// <param name="precisionInverseScale">The inverse scale of the precision.</param>
         public NormalGamma(double meanLocation, double meanScale, double precisionShape, double precisionInverseScale)
         {
-            SetParameters(meanLocation, meanScale, precisionShape, precisionInverseScale);
             _random = new Random();
+            SetParameters(meanLocation, meanScale, precisionShape, precisionInverseScale);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="NormalGamma"/> class. 
+        /// </summary>
+        /// <param name="meanLocation">The location of the mean.</param>
+        /// <param name="meanScale">The scale of the mean.</param>
+        /// <param name="precisionShape">The shape of the precision.</param>
+        /// <param name="precisionInverseScale">The inverse scale of the precision.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public NormalGamma(double meanLocation, double meanScale, double precisionShape, double precisionInverseScale, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
+            SetParameters(meanLocation, meanScale, precisionShape, precisionInverseScale);
         }
 
         /// <summary>
@@ -279,11 +281,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         public Random RandomSource
         {
-            get
-            {
-                return _random;
-            }
-
+            get { return _random; }
             set
             {
                 if (value == null)

src/Numerics/Distributions/Multivariate/Wishart.cs

@@ -69,16 +69,24 @@ namespace MathNet.Numerics.Distributions
         /// <summary>
         /// Initializes a new instance of the <see cref="Wishart"/> class. 
         /// </summary>
-        /// <param name="nu">
-        /// The degrees of freedom for the Wishart distribution.
-        /// </param>
-        /// <param name="s">
-        /// The scale matrix for the Wishart distribution.
-        /// </param>
+        /// <param name="nu">The degrees of freedom for the Wishart distribution.</param>
+        /// <param name="s">The scale matrix for the Wishart distribution.</param>
         public Wishart(double nu, Matrix<double> s)
         {
+            _random = new Random();
+            SetParameters(nu, s);
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Wishart"/> class. 
+        /// </summary>
+        /// <param name="nu">The degrees of freedom for the Wishart distribution.</param>
+        /// <param name="s">The scale matrix for the Wishart distribution.</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        public Wishart(double nu, Matrix<double> s, Random randomSource)
+        {
+            _random = randomSource ?? new Random();
             SetParameters(nu, s);
-            RandomSource = new Random();
         }
 
         /// <summary>
@@ -174,11 +182,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         public Random RandomSource
         {
-            get
-            {
-                return _random;
-            }
-
+            get { return _random; }
             set
             {
                 if (value == null)

src/Numerics/Numerics.csproj

@@ -384,8 +384,6 @@
     <Compile Include="IntegralTransforms\HartleyOptions.cs" />
     <Compile Include="GlobalizationHelper.cs" />
     <Compile Include="Interpolation\Algorithms\EquidistantPolynomialInterpolation.cs" />
-    <Compile Include="IPrecisionSupport.cs" />
-    <Compile Include="Interpolation\EquidistantPolynomialInterpolation.cs" />
     <Compile Include="IntegralTransforms\Algorithms\DiscreteFourierTransform.Options.cs" />
     <Compile Include="IntegralTransforms\Algorithms\DiscreteFourierTransform.Bluestein.cs" />
     <Compile Include="IntegralTransforms\Algorithms\DiscreteFourierTransform.Naive.cs" />

src/Numerics/Statistics/MCMC/HybridMC.cs

@@ -184,10 +184,7 @@ namespace MathNet.Numerics.Statistics.Mcmc
         private void Initialize(double[] x0)
         {
             Current = (double[])x0.Clone();
-            _pDistribution = new Normal(0, 1)
-                {
-                    RandomSource = RandomSource
-                };
+            _pDistribution = new Normal(0.0, 1.0, RandomSource);
         }
 
         /// <summary>

src/Numerics/Statistics/MCMC/UnivariateHybridMC.cs

@@ -162,9 +162,8 @@ namespace MathNet.Numerics.Statistics.Mcmc
             : base(x0, pdfLnP, frogLeapSteps, stepSize, burnInterval, randomSource, diff)
         {
             MomentumStdDev = pSdv;
-            _distribution = new Normal(0, MomentumStdDev) {RandomSource = RandomSource};
+            _distribution = new Normal(0.0, MomentumStdDev, RandomSource);
             Burn(BurnInterval);
-
         }
 
         /// <summary>

src/UnitTests/IntegralTransformsTests/FourierTest.cs

@@ -43,12 +43,9 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <summary>
         /// Continuous uniform distribution.
         /// </summary>
-        private IContinuousDistribution GetUniform(int seed)
+        IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1)
-            {
-                RandomSource = new Random(seed)
-            };
+            return new ContinuousUniform(-1, 1, new Random(seed));
         }
 
         /// <summary>
@@ -98,18 +95,18 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
             var dft = new DiscreteFourierTransform();
 
             Assert.Throws(
-                typeof(ArgumentException),
+                typeof (ArgumentException),
                 () => dft.Radix2Forward(samples, FourierOptions.Default));
 
             Assert.Throws(
-                typeof(ArgumentException),
+                typeof (ArgumentException),
                 () => dft.Radix2Inverse(samples, FourierOptions.Default));
 
             Assert.Throws(
-                typeof(ArgumentException),
+                typeof (ArgumentException),
                 () => DiscreteFourierTransform.Radix2(samples, -1));
             Assert.Throws(
-                typeof(ArgumentException),
+                typeof (ArgumentException),
                 () => DiscreteFourierTransform.Radix2Parallel(samples, -1));
         }
     }

src/UnitTests/IntegralTransformsTests/HartleyTest.cs

@@ -43,12 +43,9 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <summary>
         /// Continuous uniform distribution.
         /// </summary>
-        private IContinuousDistribution GetUniform(int seed)
+        IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1)
-            {
-                RandomSource = new Random(seed)
-            };
+            return new ContinuousUniform(-1, 1, new Random(seed));
         }
 
         /// <summary>
@@ -59,7 +56,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <param name="inverse">Is inverse.</param>
         /// <param name="dft">DFT function delegate.</param>
         /// <param name="hartley">Hartley transform delegate.</param>
-        private static void VerifyMatchesDft(
+        static void VerifyMatchesDft(
             double[] samples,
             double maximumError,
             bool inverse,

src/UnitTests/IntegralTransformsTests/InverseTransformTest.cs

@@ -43,12 +43,9 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <summary>
         /// Continuous uniform distribution.
         /// </summary>
-        private IContinuousDistribution GetUniform(int seed)
+        IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1)
-                {
-                    RandomSource = new Random(seed)
-                };
+            return new ContinuousUniform(-1, 1, new Random(seed));
         }
 
         /// <summary>
@@ -58,7 +55,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <param name="maximumError">Maximum error value.</param>
         /// <param name="forward">Forward delegate.</param>
         /// <param name="inverse">Inverse delegate.</param>
-        private void VerifyIsReversibleComplex(
+        void VerifyIsReversibleComplex(
             int count,
             double maximumError,
             Func<Complex[], Complex[]> forward,
@@ -84,7 +81,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <param name="maximumError">Maximum error value.</param>
         /// <param name="forward">Forward delegate.</param>
         /// <param name="inverse">Inverse delegate.</param>
-        private void VerifyIsReversibleReal(
+        void VerifyIsReversibleReal(
             int count,
             double maximumError,
             Func<double[], double[]> forward,
@@ -134,15 +131,15 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
                 0x8000,
                 1e-12,
                 s =>
-                {
-                    dft.Radix2Forward(s, options);
-                    return s;
-                },
+                    {
+                        dft.Radix2Forward(s, options);
+                        return s;
+                    },
                 s =>
-                {
-                    dft.Radix2Inverse(s, options);
-                    return s;
-                });
+                    {
+                        dft.Radix2Inverse(s, options);
+                        return s;
+                    });
         }
 
         /// <summary>
@@ -159,15 +156,15 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
                 0x7FFF,
                 1e-12,
                 s =>
-                {
-                    dft.BluesteinForward(s, options);
-                    return s;
-                },
+                    {
+                        dft.BluesteinForward(s, options);
+                        return s;
+                    },
                 s =>
-                {
-                    dft.BluesteinInverse(s, options);
-                    return s;
-                });
+                    {
+                        dft.BluesteinInverse(s, options);
+                        return s;
+                    });
         }
 
         /// <summary>

src/UnitTests/IntegralTransformsTests/MatchingNaiveTransformTest.cs

@@ -43,12 +43,9 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <summary>
         /// Continuous uniform distribution.
         /// </summary>
-        private IContinuousDistribution GetUniform(int seed)
+        IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1)
-            {
-                RandomSource = new Random(seed)
-            };
+            return new ContinuousUniform(-1, 1, new Random(seed));
         }
 
         /// <summary>
@@ -58,7 +55,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <param name="maximumError">Maximum error.</param>
         /// <param name="naive">Naive transform.</param>
         /// <param name="fast">Fast delegate.</param>
-        private static void VerifyMatchesNaiveComplex(
+        static void VerifyMatchesNaiveComplex(
             Complex[] samples,
             double maximumError,
             Func<Complex[], Complex[]> naive,

src/UnitTests/IntegralTransformsTests/ParsevalTheoremTest.cs

@@ -26,6 +26,7 @@
 
 namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
 {
+    using System;
     using System.Linq;
     using System.Numerics;
     using Distributions;
@@ -44,12 +45,9 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         /// <summary>
         /// Continuous uniform distribution.
         /// </summary>
-        private IContinuousDistribution GetUniform(int seed)
+        IContinuousDistribution GetUniform(int seed)
         {
-            return new ContinuousUniform(-1, 1)
-            {
-                RandomSource = new System.Random(seed)
-            };
+            return new ContinuousUniform(-1, 1, new Random(seed));
         }
 
         /// <summary>
@@ -85,7 +83,7 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
         {
             var samples = SignalGenerator.Random(x => x, GetUniform(1), count);
 
-            var timeSpaceEnergy = (from s in samples select s * s).Mean();
+            var timeSpaceEnergy = (from s in samples select s*s).Mean();
 
             var work = new double[samples.Length];
             samples.CopyTo(work, 0);
@@ -94,8 +92,9 @@ namespace MathNet.Numerics.UnitTests.IntegralTransformsTests
             var dht = new DiscreteHartleyTransform();
             work = dht.NaiveForward(work, HartleyOptions.Default);
 
-            var frequencySpaceEnergy = (from s in work select s * s).Mean();
+            var frequencySpaceEnergy = (from s in work select s*s).Mean();
             Assert.AreEqual(timeSpaceEnergy, frequencySpaceEnergy, 1e-12);
         }
     }
 }
+

src/UnitTests/InterpolationTests/LinearInterpolationCase.cs

@@ -52,10 +52,7 @@ namespace MathNet.Numerics.UnitTests.InterpolationTests
         public static void Build(out double[] x, out double[] y, out double[] xtest, out double[] ytest, int samples = 3, double sampleOffset = -0.5, double slope = 2.0, double intercept = -1.0)
         {
             // Fixed-seed "random" distribution to ensure we always test with the same data
-            var uniform = new ContinuousUniform
-                          {
-                              RandomSource = new MersenneTwister(42)
-                          };
+            var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister(42));
 
             // build linear samples
             x = new double[samples];

src/UnitTests/LinearAlgebraTests/Complex/MatrixLoader.cs

@@ -116,10 +116,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
         public static Matrix GenerateRandomDenseMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -140,10 +137,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
         public static Matrix<Complex> GenerateRandomPositiveDefiniteHermitianDenseMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -165,10 +159,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
         public static Vector GenerateRandomDenseVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new DenseVector(order);
             for (var i = 0; i < order; i++)
             {
@@ -187,10 +178,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
         public static Matrix GenerateRandomUserDefinedMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -211,10 +199,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
         public static Matrix<Complex> GenerateRandomPositiveDefiniteHermitianUserDefinedMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -236,10 +221,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
         public static Vector GenerateRandomUserDefinedVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new UserDefinedVector(order);
             for (var i = 0; i < order; i++)
             {

src/UnitTests/LinearAlgebraTests/Complex/MatrixStructureTheory.cs

@@ -77,7 +77,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
 
         protected override Matrix<Complex> CreateDenseRandom(int rows, int columns, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseMatrix(rows, columns, Enumerable.Range(0, rows*columns).Select(k => new Complex(dist.Sample(), dist.Sample())).ToArray());
         }
 
@@ -93,7 +93,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex
 
         protected override Vector<Complex> CreateVectorRandom(int size, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseVector(Enumerable.Range(0, size).Select(k => new Complex(dist.Sample(), dist.Sample())).ToArray());
         }
     }

src/UnitTests/LinearAlgebraTests/Complex32/MatrixLoader.cs

@@ -116,10 +116,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
         public static Matrix GenerateRandomDenseMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -140,10 +137,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
         public static Matrix<Complex32> GenerateRandomPositiveDefiniteHermitianDenseMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -165,10 +159,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
         public static Vector GenerateRandomDenseVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new DenseVector(order);
             for (var i = 0; i < order; i++)
             {
@@ -187,10 +178,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
         public static Matrix GenerateRandomUserDefinedMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -211,10 +199,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
         public static Matrix<Complex32> GenerateRandomPositiveDefiniteHermitianUserDefinedMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -236,10 +221,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
         public static Vector GenerateRandomUserDefinedVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new UserDefinedVector(order);
             for (var i = 0; i < order; i++)
             {

src/UnitTests/LinearAlgebraTests/Complex32/MatrixStructureTheory.cs

@@ -77,7 +77,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
 
         protected override Matrix<Complex32> CreateDenseRandom(int rows, int columns, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseMatrix(rows, columns, Enumerable.Range(0, rows*columns).Select(k => new Complex32((float) dist.Sample(), (float) dist.Sample())).ToArray());
         }
 
@@ -93,7 +93,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Complex32
 
         protected override Vector<Complex32> CreateVectorRandom(int size, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseVector(Enumerable.Range(0, size).Select(k => new Complex32((float) dist.Sample(), (float) dist.Sample())).ToArray());
         }
     }

src/UnitTests/LinearAlgebraTests/Double/MatrixLoader.cs

@@ -115,10 +115,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
         public static Matrix GenerateRandomDenseMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -139,10 +136,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
         public static Matrix<double> GenerateRandomPositiveDefiniteDenseMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -164,10 +158,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
         public static Vector GenerateRandomDenseVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new DenseVector(order);
             for (var i = 0; i < order; i++)
             {
@@ -186,10 +177,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
         public static Matrix GenerateRandomUserDefinedMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -210,10 +198,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
         public static Matrix<double> GenerateRandomPositiveDefiniteUserDefinedMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -235,10 +220,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
         public static Vector GenerateRandomUserDefinedVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new UserDefinedVector(order);
             for (var i = 0; i < order; i++)
             {

src/UnitTests/LinearAlgebraTests/Double/MatrixStructureTheory.cs

@@ -76,7 +76,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
 
         protected override Matrix<double> CreateDenseRandom(int rows, int columns, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseMatrix(rows, columns, dist.Samples().Take(rows*columns).ToArray());
         }
 
@@ -92,7 +92,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Double
 
         protected override Vector<double> CreateVectorRandom(int size, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseVector(dist.Samples().Take(size).ToArray());
         }
     }

src/UnitTests/LinearAlgebraTests/Single/MatrixLoader.cs

@@ -115,10 +115,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
         public static Matrix GenerateRandomDenseMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -139,10 +136,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
         public static Matrix<float> GenerateRandomPositiveDefiniteDenseMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new DenseMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -164,10 +158,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
         public static Vector GenerateRandomDenseVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new DenseVector(order);
             for (var i = 0; i < order; i++)
             {
@@ -186,10 +177,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
         public static Matrix GenerateRandomUserDefinedMatrix(int row, int col)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(row, col);
             for (var i = 0; i < row; i++)
             {
@@ -210,10 +198,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
         public static Matrix<float> GenerateRandomPositiveDefiniteUserDefinedMatrix(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var matrixA = new UserDefinedMatrix(order);
             for (var i = 0; i < order; i++)
             {
@@ -235,10 +220,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
         public static Vector GenerateRandomUserDefinedVector(int order)
         {
             // Fill a matrix with standard random numbers.
-            var normal = new Normal
-                {
-                    RandomSource = new MersenneTwister(1)
-                };
+            var normal = new Normal(new MersenneTwister(1));
             var v = new UserDefinedVector(order);
             for (var i = 0; i < order; i++)
             {

src/UnitTests/LinearAlgebraTests/Single/MatrixStructureTheory.cs

@@ -76,7 +76,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
 
         protected override Matrix<float> CreateDenseRandom(int rows, int columns, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseMatrix(rows, columns, dist.Samples().Select(d => (float) d).Take(rows*columns).ToArray());
         }
 
@@ -92,7 +92,7 @@ namespace MathNet.Numerics.UnitTests.LinearAlgebraTests.Single
 
         protected override Vector<float> CreateVectorRandom(int size, int seed)
         {
-            var dist = new Normal {RandomSource = new MersenneTwister(seed)};
+            var dist = new Normal(new MersenneTwister(seed));
             return new DenseVector(dist.Samples().Select(d => (float) d).Take(size).ToArray());
         }
     }

src/UnitTests/StatisticsTests/MCMCTests/RejectionSamplerTests.cs

@@ -48,11 +48,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests.McmcTests
         [Test]
         public void RejectTest()
         {
-            var uniform = new ContinuousUniform(0.0, 1.0)
-                {
-                    RandomSource = new MersenneTwister()
-                };
-
+            var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister());
             var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => 0.021, uniform.Sample);
             Assert.IsNotNull(rs.RandomSource);
 
@@ -66,10 +62,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests.McmcTests
         [Test]
         public void SampleTest()
         {
-            var uniform = new ContinuousUniform(0.0, 1.0)
-                {
-                    RandomSource = new MersenneTwister()
-                };
+            var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister());
 
             var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => 0.021, uniform.Sample)
                 {
@@ -85,10 +78,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests.McmcTests
         [Test]
         public void SampleArrayTest()
         {
-            var uniform = new ContinuousUniform(0.0, 1.0)
-                {
-                    RandomSource = new MersenneTwister()
-                };
+            var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister());
 
             var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => 0.021, uniform.Sample)
                 {
@@ -104,11 +94,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests.McmcTests
         [Test]
         public void NoUpperBound()
         {
-            var uniform = new ContinuousUniform(0.0, 1.0)
-                {
-                    RandomSource = new MersenneTwister()
-                };
-
+            var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister());
             var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => Double.NegativeInfinity, uniform.Sample);
             Assert.Throws<ArgumentOutOfRangeException>(() => rs.Sample());
         }
@@ -119,10 +105,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests.McmcTests
         [Test]
         public void NullRandomNumberGenerator()
         {
-            var uniform = new ContinuousUniform(0.0, 1.0)
-                {
-                    RandomSource = new MersenneTwister()
-                };
+            var uniform = new ContinuousUniform(0.0, 1.0, new MersenneTwister());
             var rs = new RejectionSampler<double>(x => Math.Pow(x, 1.7)*Math.Pow(1.0 - x, 5.3), x => Double.NegativeInfinity, uniform.Sample);
             Assert.Throws<ArgumentNullException>(() => rs.RandomSource = null);
         }

src/UnitTests/StatisticsTests/StatisticsTests.cs

@@ -28,6 +28,8 @@
 // OTHER DEALINGS IN THE SOFTWARE.
 // </copyright>
 
+using MathNet.Numerics.Random;
+
 namespace MathNet.Numerics.UnitTests.StatisticsTests
 {
     using System;
@@ -557,10 +559,7 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
         public void StabilityMeanVariance()
         {
             // Test around 10^9, potential stability issues
-            var gaussian = new Distributions.Normal(1e+9, 2)
-                {
-                    RandomSource = new Numerics.Random.MersenneTwister(100)
-                };
+            var gaussian = new Distributions.Normal(1e+9, 2, new MersenneTwister(100));
 
             AssertHelpers.AlmostEqual(1e+9, Statistics.Mean(gaussian.Samples().Take(10000)), 11);
             AssertHelpers.AlmostEqual(4d, Statistics.Variance(gaussian.Samples().Take(10000)), 1);