represents a Wishart matrix distribution with ν degrees of freedom and covariance matrix Σ.


  • WishartMatrixDistribution is the distribution of the sample covariance from ν independent realizations of a multivariate Gaussian distribution with covariance matrix Σ when the degrees of freedom parameter ν is an integer.
  • WishartMatrixDistribution is also known as WishartLaguerre ensemble.
  • The probability density for a symmetric matrix in a Wishart matrix distribution is proportional to , where is the size of matrix Σ.
  • The covariance matrix can be any positive definite symmetric matrix of dimensions and ν can be any real number greater than .
  • WishartMatrixDistribution can be used with such functions as MatrixPropertyDistribution, EstimatedDistribution, and RandomVariate.


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Basic Examples  (3)

Generate a pseudorandom matrix:

Check that it is symmetric and positive definite:

Sample eigenvalues of a Wishart random matrix using MatrixPropertyDistribution:

Estimate joint distribution of eigenvalues:

Mean and variance:

Scope  (6)

Generate a single pseudorandom matrix:

Generate a set of pseudorandom matrices:

Sample at extended precision:

Compute statistical properties numerically:

Numerically approximate expectation of the largest matrix eigenvalue :

Distribution parameters estimation:

Estimate the distribution parameters from sample data:

Compare LogLikelihood for both distributions:

Skewness and kurtosis:

Applications  (2)

When n and p (the dimension of the covariance matrix Σ) are both large, the scaled largest eigenvalue of a matrix from a Wishart ensemble with identity covariance is approximately distributed as a TracyWidom distribution:

Sample the scaled largest eigenvalue:

Check goodness of fit with TracyWidomDistribution:

Algebraically independent components of a symmetric Wishart matrix have a known PDF:

Build the distribution of independent components of a Wishart matrix:

Find the joint distribution of a diagonal element:

Use MatrixPropertyDistribution to sample diagonal elements of Wishart matrices:

Check goodness of fit:

Properties & Relations  (4)

Use MatrixPropertyDistribution to represent the scaled eigenvalues of a Wishart random matrix with identity covariance:

The limiting distribution of eigenvalues follows MarchenkoPasturDistribution:

Compare the histogram of the eigenvalues with the PDF:

The expression n x.TemplateBox[{m}, Inverse].x, where and are, respectively, an independent Gaussian vector and Wishart matrix, follows HotellingTSquareDistribution:

Use MatrixPropertyDistribution to sample expressions n x.TemplateBox[{m}, Inverse].x:

Diagonal elements of a Wishart random matrix each follow a scaled χ2 distribution:

Test against applicably scaled χ2 distributions:

Diagonal elements are not independent:

For any nonzero vector and Wishart matrix with scale matrix , is χ2 distributed:

Wolfram Research (2015), WishartMatrixDistribution, Wolfram Language function, https://reference.wolfram.com/language/ref/WishartMatrixDistribution.html (updated 2017).


Wolfram Research (2015), WishartMatrixDistribution, Wolfram Language function, https://reference.wolfram.com/language/ref/WishartMatrixDistribution.html (updated 2017).


Wolfram Language. 2015. "WishartMatrixDistribution." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2017. https://reference.wolfram.com/language/ref/WishartMatrixDistribution.html.


Wolfram Language. (2015). WishartMatrixDistribution. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/WishartMatrixDistribution.html


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@online{reference.wolfram_2024_wishartmatrixdistribution, organization={Wolfram Research}, title={WishartMatrixDistribution}, year={2017}, url={https://reference.wolfram.com/language/ref/WishartMatrixDistribution.html}, note=[Accessed: 18-June-2024 ]}