Matrixvariate Distributions¶
Matrixvariate distributions are the distributions whose variate forms are Matrixvariate
(i.e each sample is a matrix). Abstract types for matrixvariate distributions:
Common Interface¶
Both distributions implement the same set of methods:

size
(d)¶ The size of each sample from the distribution
d
.

length
(d)¶ The length (i.e number of elements) of each sample from the distribution
d
.

mean
(d)¶ Return the mean matrix of
d
.

pdf
(d, x)¶ Compute the probability density at the input matrix
x
.

logpdf
(d, x)¶ Compute the logarithm of the probability density at the input matrix
x
.

rand
(d)¶ Draw a sample matrix from the distribution
d
.
Wishart Distribution¶
The Wishart distribution is a multidimensional generalization of the Chisquare distribution, which is characterized by a degree of freedom ν, and a base matrix S.
Wishart(nu, S) # Wishart distribution with nu degrees of freedom and base matrix S.
InverseWishart Distribution¶
The Inverse Wishart distribution is usually used a 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.
InverseWishart(nu, P) # InverseWishart distribution with nu degrees of freedom and base matrix P.