MultivariateNormalDistribution Class

Extreme Optimization Numerical Libraries for .NET Professional

Represents a multivariate normal distribution.

Inheritance Hierarchy

SystemObject
Extreme.Statistics.DistributionsMultivariateContinuousDistribution
Extreme.Statistics.DistributionsMultivariateNormalDistribution

Namespace: Extreme.Statistics.Distributions
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1

Syntax

C++

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[SerializableAttribute]
public sealed class MultivariateNormalDistribution : MultivariateContinuousDistribution

<SerializableAttribute>
Public NotInheritable Class MultivariateNormalDistribution
	Inherits MultivariateContinuousDistribution

[SerializableAttribute]
public ref class MultivariateNormalDistribution sealed : public MultivariateContinuousDistribution

[<SealedAttribute>]
[<SerializableAttribute>]
type MultivariateNormalDistribution =  
    class
        inherit MultivariateContinuousDistribution
    end

The MultivariateNormalDistribution type exposes the following members.

Constructors

	Name	Description
	MultivariateNormalDistribution(Int32)	Constructs a new MultivariateNormalDistribution with mean equal to zero and standard deviation equal to 1.
	MultivariateNormalDistribution(MatrixDouble)	Estimates the parameters of the distribution of a set of observations assuming it follows a multivariate normal distribution.
	MultivariateNormalDistribution(VectorDouble)	Constructs a new MultivariateNormalDistribution with specified mean and standard deviation equal to 1.
	MultivariateNormalDistribution(VectorDouble)	Estimates the parameters of the distribution of a variable assuming it follows a normal distribution.
	MultivariateNormalDistribution(VectorDouble, MatrixDouble)	Constructs a new MultivariateNormalDistribution with specified mean and standard deviation.

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Properties

	Name	Description
	Order	Gets the number of dimensions of the distribution. (Inherited from MultivariateContinuousDistribution.)

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Methods

	Name	Description
	CreateStandard	Represents the standard MultivariateNormalDistribution.
	DistributionFunction	Returns the cumulative distribution function (CDF) of this distribution for the specified value.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	FillSample(Random, VectorDouble)	Fills a vector with a random sample from the distribution. (Inherited from MultivariateContinuousDistribution.)
	FillSample(Random, Double)	Fills a Double array with random numbers. (Inherited from MultivariateContinuousDistribution.)
	FillSamples	Fills the rows of a matrix with random samples from the distribution. (Inherited from MultivariateContinuousDistribution.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetMeans	Returns the mean or expectation value of the distribution. (Overrides MultivariateContinuousDistributionGetMeans.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetVarianceCovarianceMatrix	Returns the variance of the distribution. (Overrides MultivariateContinuousDistributionGetVarianceCovarianceMatrix.)
	LogProbabilityDensityFunction	Returns the natural logarithm of the probability density function (PDF) of this distribution for the specified value. (Overrides MultivariateContinuousDistributionLogProbabilityDensityFunction(VectorDouble).)
	ProbabilityDensityFunction	Returns the value of the probability density function (PDF) of this distribution for the specified value. (Inherited from MultivariateContinuousDistribution.)
	Sample(Random)	Returns a random sample from the distribution. (Inherited from MultivariateContinuousDistribution.)
	Sample(Random, Int32)	Returns a matrix with random samples from the distribution. (Inherited from MultivariateContinuousDistribution.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Remarks

The multivariate normal distribution is a generalization of the normal distribution. The mean of the multivariate distribution is a vector. The multivariate equivalent of the variance is the variance-covariance matrix, which must be positive semi-definite.

Reference

Extreme.Statistics.Distributions Namespace

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