RandomExtensions.CorrelatedSamples Method

public static Vector<double>[] CorrelatedSamples(
	this Random random,
	int numberOfSamples,
	Distribution[] distributions,
	SymmetricMatrix<double> correlations
)

<ExtensionAttribute>
Public Shared Function CorrelatedSamples ( 
	random As Random,
	numberOfSamples As Integer,
	distributions As Distribution(),
	correlations As SymmetricMatrix(Of Double)
) As Vector(Of Double)()

public:
[ExtensionAttribute]
static array<Vector<double>^>^ CorrelatedSamples(
	Random^ random, 
	int numberOfSamples, 
	array<Distribution^>^ distributions, 
	SymmetricMatrix<double>^ correlations
)

[<ExtensionAttribute>]
static member CorrelatedSamples : 
        random : Random * 
        numberOfSamples : int * 
        distributions : Distribution[] * 
        correlations : SymmetricMatrix<float> -> Vector<float>[] 

Parameters

random  Random

A random number generator used to generate samples from the distributions.

numberOfSamples  Int32

The number of

distributions  Distribution[]

An array of Distribution objects that specifies the probability distribution of each variable.

correlations  SymmetricMatrix<Double>

A SymmetricMatrix<T> that specifies the desired correlation matrix.

Return Value

Usage Note

Use this method to generate a series of correlated numerical variables. Each variable is composed of random samples taken from the corresponding distribution in the distributions array. Both continuous and discrete distributions are allowed. correlations must be a correlation matrix. It must be positive definite with ones on the diagonal. The size of the correlation matrix must match the length of distributions. The number of samples must be at least two.

The correlation matrix of the variables will be approximately equal to correlations. Larger sample sizes will generally yield a better approximation, especially if the probability distributions are fairly symmetrical. For very asymmetrical distributions, it may be difficult to produce the desired correlation.

This method uses the algorithm of Iman-Conover.

Reference