Understanding matrixvariate distributions

This is a distribution from which any sample drawn is of type matrix. Many of the methods that can be used with Univariate and Multivariate distributions can be used with Matrix-variate distributions.

Wishart distribution

This is a type of matrix-variate distribution and is a generalization of the Chi-square distribution to two or more variables. It is constructed by adding the inner products of identically distributed, independent, and zero-mean multivariate normal random vectors. It is used as a model for the distribution of the sample covariance matrix for multivariate normal random data, after scaling by the sample size:

julia> Wishart(v, S) 

Here, v refers to the degrees of freedom and S is the base matrix.

Inverse-Wishart distribution

This is the conjugate prior to the covariance matrix of a multivariate normal distribution. In Julia, it is implemented as follows:

julia> InverseWishart(v, P) 

This represents an Inverse-Wishart...