MNmargLike: Marginal Likelihood for the Multivariate Normal Model.

In mvst: Bayesian Inference for the Multivariate Skew-t Model

Description

This function computes the exact marginal likelihood for Normally distributed data, under the default priors.

Usage

MNmargLike(y, X=NULL, LOG=FALSE)

Arguments

y	data matrix.
X	(optional) a design matrix.
LOG	logical; if TRUE, the log-marginal likelihood is returned.

Value

A scalar representing the marginal likelihood of a (multivariate) Normal model under the default priors for data y. If the design matrix X is provided, the function returns the marginal likelihood of a (multivariate) regression model with Normally distributed errors.

References

Liseo B, Parisi A (2013). Bayesian Inference for the Multivariate Skew-Normal Model: A Population Monte Carlo approach. Comput. Statist. Data Anal., 63, 125-138. ISSN 0167-9473. doi:10.1016/j.csda.2013.02.007.

See Also

rmvSE, dmvSE.

Examples

# Generate Normally distributed data
require(mvtnorm)
y = rmvnorm(100, rep(2,2), diag(2))
# Marginal likelihood (exact value)
MNmargLike(y, X=NULL, LOG=TRUE)

mvst documentation built on May 2, 2019, 1:46 p.m.