Matrix Gamma Distribution

Description

This function provides the density for the matrix gamma distribution.

Usage

1
dmatrixgamma(X, alpha, beta, Sigma, log=FALSE)

Arguments

X

This is a k x k positive-definite precision matrix.

alpha

This is a scalar shape parameter (the degrees of freedom), alpha.

beta

This is a scalar, positive-only scale parameter, beta.

Sigma

This is a k x k positive-definite scale matrix.

log

Logical. If log=TRUE, then the logarithm of the density is returned.

Details

  • Application: Continuous Multivariate Matrix

  • Density: p(theta) = {|Sigma|^(-alpha) / [beta^(k alpha) Gamma[k](alpha)]} |theta|^[alpha-(k+1)/2] exp(tr(-(1/beta)Sigma^(-1)theta))

  • Inventors: Unknown

  • Notation 1: theta ~ MG[k](alpha, beta, Sigma)

  • Notation 2: p(theta) = MG[k](theta | alpha, beta, Sigma)

  • Parameter 1: shape alpha > 2

  • Parameter 2: scale beta > 0

  • Parameter 3: positive-definite k x k scale matrix Sigma

  • Mean:

  • Variance:

  • Mode:

The matrix gamma (MG), also called the matrix-variate gamma, distribution is a generalization of the gamma distribution to positive-definite matrices. It is a more general and flexible version of the Wishart distribution (dwishart), and is a conjugate prior of the precision matrix of a multivariate normal distribution (dmvnp) and matrix normal distribution (dmatrixnorm).

The compound distribution resulting from compounding a matrix normal with a matrix gamma prior over the precision matrix is a generalized matrix t-distribution.

The matrix gamma distribution is identical to the Wishart distribution when alpha = nu / 2 and beta = 2.

Value

dmatrixgamma gives the density.

Author(s)

Statisticat, LLC. software@bayesian-inference.com

See Also

dgamma dmatrixnorm, dmvnp, and dwishart

Examples

1
2
3
4
library(LaplacesDemon)
k <- 10
dmatrixgamma(X=diag(k), alpha=(k+1)/2, beta=2, Sigma=diag(k), log=TRUE)
dwishart(Omega=diag(k), nu=k+1, S=diag(k), log=TRUE)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.