Density and sampling for the Matrix-t distribution.
dMT(X, Lambda, SigmaR, SigmaC, nu, log = FALSE)
rMT(n, Lambda, SigmaR, SigmaC, nu)Argument to the density function. Either a p x q matrix or a p x q x n array.
Mean parameter. Either a p x q matrix or a p x q x n array.
Between-row covariance matrix. Either a p x p matrix or a p x p x n array.
Between-column covariance matrix Either a q x q matrix or a q x q x n array.
Degrees-of-freedom parameter. A scalar or vector of length n.
Logical; whether or not to compute the log-density.
Integer number of random samples to generate.
A vector length n for density evaluation, or an array of size p x q x n for random sampling.
The Matrix-T distribution \(\boldsymbol{X} \sim \textrm{Matrix-T}(\boldsymbol{\Lambda}, \boldsymbol{\Sigma}, \boldsymbol{\Psi}, \nu)\) on a random matrix \(\boldsymbol{X}_{p \times q}\) is the marginal distribution of \(\boldsymbol{X}\) in \((\boldsymbol{X}, \boldsymbol{V}) \sim \textrm{MNIW}(\boldsymbol{\Lambda}, \boldsymbol{\Sigma}, \boldsymbol{\Psi}, \nu)\), where the Matrix-Normal Inverse-Wishart (MNIW) distribution is defined in MNIW-dist.