dist.Matrix.Gamma: Matrix Gamma Distribution

Description Usage Arguments Details Value Author(s) See Also Examples

Description

This function provides the density for the matrix gamma distribution.

Usage

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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

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

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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)

Example output

[1] -82.3189
[1] -82.3189

LaplacesDemon documentation built on July 9, 2021, 5:07 p.m.