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.