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

InverseWishartDistribution 
Creates a new Inverse Wishart distribution.

Name  Description  

Covariance 
Gets the variancecovariance matrix for this distribution.
(Overrides MatrixContinuousDistributionCovariance.)  
Dimension 
Gets the number of variables for this distribution.
(Inherited from MatrixContinuousDistribution.)  
Mean 
Gets the mean for this distribution.
(Overrides MatrixContinuousDistributionMean.)  
Median 
Gets the median for this distribution.
(Inherited from MatrixContinuousDistribution.)  
Mode 
Gets the mode for this distribution.
(Inherited from MatrixContinuousDistribution.)  
NumberOfColumns 
Gets the number of columns that matrices from this distribution should have.
(Inherited from MatrixContinuousDistribution.)  
NumberOfRows 
Gets the number of rows that matrices from this distribution should have.
(Inherited from MatrixContinuousDistribution.)  
Variance 
Gets the variance for this distribution.
(Overrides MatrixContinuousDistributionVariance.) 
Name  Description  

Clone 
Creates a new object that is a copy of the current instance.
(Overrides DistributionBaseClone.)  
ComplementaryDistributionFunction(Double) 
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 MatrixContinuousDistribution.)  
ComplementaryDistributionFunction(Double) 
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 MatrixContinuousDistribution.)  
DistributionFunction(Double) 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Inherited from MatrixContinuousDistribution.)  
DistributionFunction(Double) 
Gets the cumulative distribution function (cdf) for
this distribution evaluated at point x.
(Inherited from MatrixContinuousDistribution.)  
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 MatrixContinuousDistribution.)  
Fit(Double) 
Fits the underlying distribution to a given set of observations.
(Inherited from MatrixContinuousDistribution.)  
Fit(Double, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from MatrixContinuousDistribution.)  
Fit(Double, Double) 
Fits the underlying distribution to a given set of observations.
(Inherited from MatrixContinuousDistribution.)  
Fit(Double, Int32) 
Fits the underlying distribution to a given set of observations.
(Inherited from MatrixContinuousDistribution.)  
Fit(Double, Double) 
Fits the underlying distribution to a given set of observations.
(Inherited from MatrixContinuousDistribution.)  
Fit(Double, Double, IFittingOptions) 
Not supported.
(Overrides MatrixContinuousDistributionFit(Double, Double, IFittingOptions).)  
Fit(Double, Int32, IFittingOptions) 
Fits the underlying distribution to a given set of observations.
(Inherited from MatrixContinuousDistribution.)  
Generate 
Generates a random observation from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Double) 
Generates a random observation from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Double) 
Generates a random observation from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Int32) 
Generates a random vector of observations from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Random) 
Generates a random observation from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Double, Random) 
Generates a random observation from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Double, Random) 
Generates a random observation from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Int32, Double) 
Generates a random vector of observations from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Int32, Double) 
Generates a random vector of observations from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Int32, Random) 
Generates a random vector of observations from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Int32, Double, Random) 
Generates a random vector of observations from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
Generate(Int32, Double, Random) 
Generates a random vector of observations from the current distribution.
(Inherited from MatrixContinuousDistribution.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
InnerComplementaryDistributionFunction 
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 MatrixContinuousDistribution.)  
InnerDistributionFunction 
Not supported.
(Overrides MatrixContinuousDistributionInnerDistributionFunction(Double).)  
InnerLogProbabilityDensityFunction 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Overrides MatrixContinuousDistributionInnerLogProbabilityDensityFunction(Double).)  
InnerProbabilityDensityFunction 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Overrides MatrixContinuousDistributionInnerProbabilityDensityFunction(Double).)  
LogProbabilityDensityFunction 
Gets the logprobability density function (pdf)
for this distribution evaluated at point x.
(Inherited from MatrixContinuousDistribution.)  
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ProbabilityDensityFunction 
Gets the probability density function (pdf) for
this distribution evaluated at point x.
(Inherited from MatrixContinuousDistribution.)  
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.)  
To(Type)  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 ExtensionMethods.) 
The inverse Wishart distribution, also called the inverted Wishart distribution, is a probability distribution defined on realvalued positivedefinite matrices. In Bayesian statistics it is used as the conjugate prior for the covariance matrix of a multivariate normal distribution.
References:
// Create a Inverse Wishart with the parameters var invWishart = new InverseWishartDistribution( // Degrees of freedom degreesOfFreedom: 4, // Scale parameter inverseScale: new double[,] { { 1.7, 0.2 }, { 0.2, 5.3 }, } ); // Common measures double[] var = invWishart.Variance; // { 3.4, 10.6 } double[,] cov = invWishart.Covariance; // see below double[,] mmean = invWishart.MeanMatrix; // see below // cov mean // 5.78 4.56 1.7 0.2 // 4.56 56.18 0.2 5.3 // (the above matrix representations have been transcribed to text using) string scov = cov.ToString(DefaultMatrixFormatProvider.InvariantCulture); string smean = mmean.ToString(DefaultMatrixFormatProvider.InvariantCulture); // For compatibility reasons, .Mean stores a flattened mean matrix double[] mean = invWishart.Mean; // { 1.7, 0.2, 0.2, 5.3 } // Probability density functions double pdf = invWishart.ProbabilityDensityFunction(new double[,] { { 5.2, 0.2 }, // 0.000029806281690351203 { 0.2, 4.2 }, }); double lpdf = invWishart.LogProbabilityDensityFunction(new double[,] { { 5.2, 0.2 }, // 10.420791391688828 { 0.2, 4.2 }, });