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:
Base.size — Method.size(d::MatrixDistribution)Return the size of each sample from distribution d.
Base.length — Method.length(d::MatrixDistribution)The length (i.e number of elements) of each sample from the distribution d.
Statistics.mean — Method.mean(d::MatrixDistribution)Return the mean matrix of d.
Distributions.pdf — Method.pdf(d::MatrixDistribution, x::AbstractArray)Compute the probability density at the input matrix x.
Distributions.logpdf — Method.logpdf(d::MatrixDistribution, AbstractMatrix)Compute the logarithm of the probability density at the input matrix x.
Distributions._rand! — Method._rand!(::AbstractRNG, ::MatrixDistribution, A::AbstractMatrix)Sample the matrix distribution and store the result in A. Must be implemented by matrix-variate distributions.
Distributions
Distributions.Wishart — Type.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.
Distributions.InverseWishart — Type.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.
Internal Methods (for creating your own matrix-variate distributions)
Distributions._logpdf — Method._logpdf(d::MatrixDistribution, x::AbstractArray)Evaluate logarithm of pdf value for a given sample x. This function need not perform dimension checking.