## 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..

### 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

#### boolIsValidParameterSet(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

#### doubleDensity(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.

#### 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.

#### stringToString()

A string representation of the distribution.
##### Return
###### `string`

a string representation of the distribution.

### Public Properties

#### doubleDegreesOfFreedom get;

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

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.
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#### RandomRandomSource 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: