Matrix-variate Distributions

Matrix-variate Distributions

Matrix-variate distributions are the distributions whose variate forms are Matrixvariate (i.e each sample is a matrix). Abstract types for matrix-variate distributions:

Common Interface

Both distributions implement the same set of methods:

size(::MatrixDistribution)
length(::MatrixDistribution)
mean(::MatrixDistribution)
pdf{T<:Real}(d::MatrixDistribution, x::AbstractMatrix{T})
logpdf{T<:Real}(d::MatrixDistribution, x::AbstractMatrix{T})
rand(::MatrixDistribution, ::Int)

Distributions

Wishart(nu, S)

The Wishart distribution is a multidimensional generalization of the Chi-square distribution, which is characterized by a degree of freedom ν, and a base matrix S.

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InverseWishart(nu, P)

The Inverse Wishart distribution is usually used as the conjugate prior for the covariance matrix of a multivariate normal distribution, which is characterized by a degree of freedom ν, and a base matrix Φ.

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Internal Methods (for creating your own matrix-variate distributions)

_logpdf(d::MatrixDistribution, x::AbstractArray)

Evaluate logarithm of pdf value for a given sample x. This function need not perform dimension checking.

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