Namespaces

Types in MathNet.Numerics.Distributions

Type InverseWishart

Namespace MathNet.Numerics.Distributions

Interfaces IDistribution

Multivariate Inverse Wishart distribution. This distribution is parameterized by the degrees of freedom nu and the scale matrix S. The inverse Wishart distribution is the conjugate prior for the covariance matrix of a multivariate normal distribution..

Constructors

Static Functions

Methods

Properties

Public Constructors

InverseWishart(double degreesOfFreedom, Matrix<T> scale)

Initializes a new instance of the InverseWishart class.
Parameters
double degreesOfFreedom

The degree of freedom (ν) for the inverse Wishart distribution.

Matrix<T> scale

The scale matrix (Ψ) for the inverse Wishart distribution.

InverseWishart(double degreesOfFreedom, Matrix<T> scale, Random randomSource)

Initializes a new instance of the InverseWishart class.
Parameters
double degreesOfFreedom

The degree of freedom (ν) for the inverse Wishart distribution.

Matrix<T> scale

The scale matrix (Ψ) for the inverse Wishart distribution.

Random randomSource

The random number generator which is used to draw random samples.

Public Static Functions

bool IsValidParameterSet(double degreesOfFreedom, Matrix<T> scale)

Tests whether the provided values are valid parameters for this distribution.
Parameters
double degreesOfFreedom

The degree of freedom (ν) for the inverse Wishart distribution.

Matrix<T> scale

The scale matrix (Ψ) for the inverse Wishart distribution.

Matrix<T> Sample(Random rnd, double degreesOfFreedom, Matrix<T> scale)

Samples an inverse Wishart distributed random variable by sampling a Wishart random variable and inverting the matrix.
Parameters
Random rnd

The random number generator to use.

double degreesOfFreedom

The degree of freedom (ν) for the inverse Wishart distribution.

Matrix<T> scale

The scale matrix (Ψ) for the inverse Wishart distribution.

Return
Matrix<T>

a sample from the distribution.

Public Methods

double Density(Matrix<T> x)

Evaluates the probability density function for the inverse Wishart distribution.
Parameters
Matrix<T> x

The matrix at which to evaluate the density at.

Return
double

the density at x.

bool Equals(object obj)

int GetHashCode()

Type GetType()

Matrix<T> Sample()

Samples an inverse Wishart distributed random variable by sampling a Wishart random variable and inverting the matrix.
Return
Matrix<T>

a sample from the distribution.

string ToString()

A string representation of the distribution.
Return
string

a string representation of the distribution.

Public Properties

double DegreesOfFreedom get;

Gets or sets the degree of freedom (ν) for the inverse Wishart distribution.

Matrix<T> Mean get;

Gets the mean.
Value:

Matrix<T> Mode get;

Gets the mode of the distribution.
A. O'Hagan, and J. J. Forster (2004). Kendall's Advanced Theory of Statistics: Bayesian Inference. 2B (2 ed.). Arnold. ISBN 0-340-80752-0.
Value:

Random RandomSource get; set;

Gets or sets the random number generator which is used to draw random samples.

Matrix<T> Scale get;

Gets or sets the scale matrix (Ψ) for the inverse Wishart distribution.

Matrix<T> Variance get;

Gets the variance of the distribution.
Kanti V. Mardia, J. T. Kent and J. M. Bibby (1979). Multivariate Analysis.
Value: