﻿Wishart Methods
 Wishart Methods
Microsoft Research

The Wishart type exposes the following members.

Methods
NameDescription
Clone
Clones this Wishart.
FromMeanAndMeanLogDeterminant
Constructs a Wishart distribution with the given mean and mean log determinant.
FromShapeAndRate(Double, PositiveDefiniteMatrix)
Creates a multi-dimensional Wishart with given shape and rate matrix
FromShapeAndRate(Int32, Double, Double)
Creates a multi-dimensional Wishart with given shape and with a rate matrix which is set to a scaled identity matrix
FromShapeAndScale(Double, PositiveDefiniteMatrix)
Creates a new multi-dimensional Wishart with given shape and scale matrix
FromShapeAndScale(Int32, Double, Double)
Creates a multi-dimensional Wishart with given shape and with a scale matrix which is set to a scaled identity matrix
GetAverageLog
The expected logarithm of that distribution under this distribution.
GetLogAverageOf
Gets the log-integral of the product of this Wishart with another Wishart
GetLogAverageOfPower
Get the integral of this distribution times another distribution raised to a power.
GetLogNormalizer
Gets the normalizer for the density function of this Wishart distribution
GetLogNormalizer(Double, PositiveDefiniteMatrix)
Gets the normalizer for a Wishart density function specified by shape and rate matrix
GetLogProb(PositiveDefiniteMatrix)
Evaluates the logarithm of this Wishart density function at a given point
GetLogProb(PositiveDefiniteMatrix, Double, PositiveDefiniteMatrix)
Evaluates the logarithm of a Wishart density function at a given point
GetMean
Gets the mean of the distribution.
GetMean(PositiveDefiniteMatrix)
Gets the mean of the distribution.
GetMeanAndVariance
Gets the mean and variance matrices.
GetMeanLogDeterminant
Gets the mean log determinant
GetMode
GetMode(PositiveDefiniteMatrix)
GetScale
Gets the scale matrix
GetVariance
Gets the variance of the distribution
GetVariance(PositiveDefiniteMatrix)
Gets the variance of the distribution
IsProper
Asks whether this instance is proper
IsProper(Double, PositiveDefiniteMatrix)
Asks whether a Wishart distribution of the specified shape and rate is proper
IsUniform
Asks whether this instance is uniform
MaxDiff
The maximum difference between the parameters of this Wishart and that Wishart
PointMass(Double)
Creates a Wishart point mass at the specified location
PointMass(PositiveDefiniteMatrix)
Creates a Wishart point mass at the specified location
Sample
Samples this Wishart distribution. Workspaces and sample matrix are allocated behind the scenes
Sample(PositiveDefiniteMatrix)
Samples this Wishart distribution. Workspaces are allocated behind the scenes
Sample(Double, PositiveDefiniteMatrix, PositiveDefiniteMatrix)
Samples a Wishart distribution of specified shape and rate. Workspaces are allocated behind the scenes
Sample(PositiveDefiniteMatrix, LowerTriangularMatrix, LowerTriangularMatrix, Matrix)
Samples this Wishart distribution
SampleFromShapeAndRate(Double, PositiveDefiniteMatrix)
Samples a Wishart distribution of specified shape and rate. Workspaces are allocated behind the scenes
SampleFromShapeAndRate(Double, PositiveDefiniteMatrix, PositiveDefiniteMatrix)
Samples a Wishart distribution of specified shape and rate. Workspaces are allocated behind the scenes
SampleFromShapeAndScale
Samples a Wishart distribution of specified shape and scale. Workspaces are allocated behind the scenes
SetDerivatives
Modify the parameters so that the pdf has the given derivatives at a point.
SetMeanAndVariance
Sets the parameters to produce a given mean and variance.
SetShapeAndRate
Sets the shape parameter and the rate matrix parameter for this instance
SetShapeAndScale
Sets the shape parameter and the scale matrix parameter for this instance
SetTo
Sets this Wishart instance to have the parameter values of another Wishart instance
SetToPower
Sets the parameters to represent the power of a source Wishart to some exponent.
SetToProduct
Sets the parameters to represent the product of two Wisharts.
SetToRatio
Sets the parameters to represent the ratio of two Wisharts.
SetToSum
Weighted mixture distribution for two Wisharts
SetToUniform
Sets this instance to have uniform distribution
Uniform
Constructs a uniform Wishart distribution of the given dimension
WeightedSumT
Creates a weighted mixture distribution for distributions whose mean and variance are both of type PositiveDefiniteMatrix. The distribution type must implement CanGetMeanAndVarianceMeanType, VarType and CanSetMeanAndVarianceMeanType, VarType
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