MultivariateNormalDistribution Constructor

public MultivariateNormalDistribution(
	int order
)

Public Sub New ( 
	order As Integer
)

public:
MultivariateNormalDistribution(
	int order
)

new : 
        order : int -> MultivariateNormalDistribution

Parameters

order  Int32

 

This constructor creates a standard normal distribution, with mean zero and standard deviation equal to one.

public MultivariateNormalDistribution(
	Matrix<double> data
)

Public Sub New ( 
	data As Matrix(Of Double)
)

public:
MultivariateNormalDistribution(
	Matrix<double>^ data
)

new : 
        data : Matrix<float> -> MultivariateNormalDistribution

Parameters

data  Matrix<Double>

A real Matrix<T> whose rows contain the observations.

Return Value

Use this constructor to create a MultivariateNormalDistribution whose parameters are estimated from data. This constructor uses an unbiased estimator. A single observation uses identity covariance. At least one observation and one variable are required.

public MultivariateNormalDistribution(
	Vector<double> mean
)

Public Sub New ( 
	mean As Vector(Of Double)
)

public:
MultivariateNormalDistribution(
	Vector<double>^ mean
)

new : 
        mean : Vector<float> -> MultivariateNormalDistribution

Parameters

mean  Vector<Double>

The nonempty vector of finite means. Its values are copied.

public MultivariateNormalDistribution(
	Vector<double>[] variables
)

Public Sub New ( 
	variables As Vector(Of Double)()
)

public:
MultivariateNormalDistribution(
	array<Vector<double>^>^ variables
)

new : 
        variables : Vector<float>[] -> MultivariateNormalDistribution

Parameters

variables  Vector<Double>[]

An array of vectors.

Return Value

Use this constructor to create a MultivariateNormalDistribution whose parameters are estimated from variables. This constructor uses an unbiased estimator. Each variable must have the same positive observation count. A single observation uses identity covariance.

public MultivariateNormalDistribution(
	Vector<double> mean,
	Matrix<double> covarianceMatrix
)

Public Sub New ( 
	mean As Vector(Of Double),
	covarianceMatrix As Matrix(Of Double)
)

public:
MultivariateNormalDistribution(
	Vector<double>^ mean, 
	Matrix<double>^ covarianceMatrix
)

new : 
        mean : Vector<float> * 
        covarianceMatrix : Matrix<float> -> MultivariateNormalDistribution

Parameters

mean  Vector<Double>

The nonempty vector of finite means. Its values are copied.

covarianceMatrix  Matrix<Double>

The finite, symmetric variance-covariance matrix. Its values are copied.

The covariance must be positive semi-definite and have one row and column for each mean.

Reference