WishartDistribution Class 
Namespace: Accord.Statistics.Distributions.Multivariate
[SerializableAttribute] public class WishartDistribution : MultivariateContinuousDistribution
The WishartDistribution type exposes the following members.
Name  Description  

WishartDistribution 
Creates a new Wishart distribution.

Name  Description  

Covariance 
Gets the variancecovariance matrix for this distribution.
(Overrides MultivariateContinuousDistributionCovariance.)  
DegreesOfFreedom 
Gets the degrees of freedom for this Wishart distribution.
 
Dimension 
Gets the number of variables for this distribution.
(Inherited from MultivariateContinuousDistribution.)  
Mean 
Gets the mean for this distribution as a flat matrix.
(Overrides MultivariateContinuousDistributionMean.)  
MeanMatrix 
Gets the mean for this distribution.
 
Median 
Gets the median for this distribution.
(Inherited from MultivariateContinuousDistribution.)  
Mode 
Gets the mode for this distribution.
(Inherited from MultivariateContinuousDistribution.)  
Variance 
Gets the variance for this distribution.
(Overrides MultivariateContinuousDistributionVariance.) 
Name  Description  

Clone 
Creates a new object that is a copy of the current instance.
(Overrides DistributionBaseClone.)  
ComplementaryDistributionFunction 
Gets the complementary cumulative distribution function
(ccdf) for this distribution evaluated at point x.
This function is also known as the Survival function.
(Inherited from MultivariateContinuousDistribution.)  
DistributionFunction 
Unsupported.
(Overrides MultivariateContinuousDistributionDistributionFunction(Double).)  
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
Fit(Double) 
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.)  
Fit(Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.)  
Fit(Double, Double) 
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.)  
Fit(Double, Int32) 
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.)  
Fit(Double, Int32, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from MultivariateContinuousDistribution.)  
Fit(Double, Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Overrides MultivariateContinuousDistributionFit(Double, Double, IFittingOptions).)  
Generate 
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.)  
Generate(Double) 
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.)  
Generate(Int32) 
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.)  
Generate(Int32) 
Generates a random observation from the current distribution.
(Inherited from MultivariateContinuousDistribution.)  
Generate(Int32, Double) 
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.)  
Generate(Int32, Int32) 
Generates a random vector of observations from the current distribution.
(Inherited from MultivariateContinuousDistribution.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
LogProbabilityDensityFunction(Double) 
Gets the logprobability density function (pdf)
for this distribution evaluated at point x.
 
LogProbabilityDensityFunction(Double) 
Gets the logprobability density function (pdf)
for this distribution evaluated at point x.
(Overrides MultivariateContinuousDistributionLogProbabilityDensityFunction(Double).)  
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ProbabilityDensityFunction(Double) 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
 
ProbabilityDensityFunction(Double) 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Overrides MultivariateContinuousDistributionProbabilityDensityFunction(Double).)  
ToString 
Returns a String that represents this instance.
(Inherited from DistributionBase.)  
ToString(IFormatProvider) 
Returns a String that represents this instance.
(Inherited from DistributionBase.)  
ToString(String) 
Returns a String that represents this instance.
(Inherited from DistributionBase.)  
ToString(String, IFormatProvider) 
Returns a String that represents this instance.
(Overrides DistributionBaseToString(String, IFormatProvider).) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual  Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by Matrix.) 
The Wishart distribution is a generalization to multiple dimensions of
the ChiSquared distribution, or, in
the case of noninteger
References:
// Create a Wishart distribution with the parameters: WishartDistribution wishart = new WishartDistribution( // Degrees of freedom degreesOfFreedom: 7, // Scale parameter scale: new double[,] { { 4, 1, 1 }, { 1, 2, 2 }, // (must be symmetric and positive definite) { 1, 2, 6 }, } ); // Common measures double[] var = wishart.Variance; // { 224, 56, 504 } double[,] cov = wishart.Covariance; // see below double[,] meanm = wishart.MeanMatrix; // see below // 224 63 175 28 7 7 // cov = 63 56 112 mean = 7 14 14 // 175 112 504 7 14 42 // (the above matrix representations have been transcribed to text using) string scov = cov.ToString(DefaultMatrixFormatProvider.InvariantCulture); string smean = meanm.ToString(DefaultMatrixFormatProvider.InvariantCulture); // For compatibility reasons, .Mean stores a flattened mean matrix double[] mean = wishart.Mean; // { 28, 7, 7, 7, 14, 14, 7, 14, 42 } // Probability density functions double pdf = wishart.ProbabilityDensityFunction(new double[,] { { 8, 3, 1 }, { 3, 7, 1 }, // 0.000000011082455043473361 { 1, 1, 8 }, }); double lpdf = wishart.LogProbabilityDensityFunction(new double[,] { { 8, 3, 1 }, { 3, 7, 1 }, // 18.317902605850534 { 1, 1, 8 }, });