## Types in MathNet.Numerics.Distributions

Type MatrixNormal

Namespace MathNet.Numerics.Distributions

Interfaces IDistribution

Multivariate Matrix-valued Normal distributions. The distribution is parameterized by a mean matrix (M), a covariance matrix for the rows (V) and a covariance matrix for the columns (K). If the dimension of M is d-by-m then V is d-by-d and K is m-by-m..

### Public Constructors

#### MatrixNormal(Matrix<T> m, Matrix<T> v, Matrix<T> k)

Initializes a new instance of the MatrixNormal class.
##### Parameters
###### `Matrix<T>` m

The mean of the matrix normal.

###### `Matrix<T>` v

The covariance matrix for the rows.

###### `Matrix<T>` k

The covariance matrix for the columns.

#### MatrixNormal(Matrix<T> m, Matrix<T> v, Matrix<T> k, Random randomSource)

Initializes a new instance of the MatrixNormal class.
##### Parameters
###### `Matrix<T>` m

The mean of the matrix normal.

###### `Matrix<T>` v

The covariance matrix for the rows.

###### `Matrix<T>` k

The covariance matrix for the columns.

###### `Random` randomSource

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

### Public Static Functions

#### boolIsValidParameterSet(Matrix<T> m, Matrix<T> v, Matrix<T> k)

Tests whether the provided values are valid parameters for this distribution.
##### Parameters
###### `Matrix<T>` m

The mean of the matrix normal.

###### `Matrix<T>` v

The covariance matrix for the rows.

###### `Matrix<T>` k

The covariance matrix for the columns.

#### Matrix<T>Sample(Random rnd, Matrix<T> m, Matrix<T> v, Matrix<T> k)

Samples a matrix normal distributed random variable.
##### Parameters
###### `Random` rnd

The random number generator to use.

###### `Matrix<T>` m

The mean of the matrix normal.

###### `Matrix<T>` v

The covariance matrix for the rows.

###### `Matrix<T>` k

The covariance matrix for the columns.

##### Return
###### `Matrix<T>`

a sequence of samples from the distribution.

### Public Methods

#### doubleDensity(Matrix<T> x)

Evaluates the probability density function for the matrix normal distribution.
##### Parameters
###### `Matrix<T>` x

The matrix at which to evaluate the density at.

the density at x

#### Matrix<T>Sample()

Samples a matrix normal distributed random variable.
##### Return
###### `Matrix<T>`

A random number from this distribution.

#### stringToString()

Returns a String that represents this instance.
##### Return
###### `string`

A String that represents this instance.

### Public Properties

#### Matrix<T>ColumnCovariance get;

Gets the column covariance. (K)
Value:

#### Matrix<T>Mean get;

Gets the mean. (M)
Value:

#### RandomRandomSource get; set;

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

#### Matrix<T>RowCovariance get;

Gets the row covariance. (V)
Value: