MNmargLike | R Documentation |
This function computes the exact marginal likelihood for Normally distributed data, under the default priors.
MNmargLike(y, X=NULL, LOG=FALSE)
y |
data matrix. |
X |
(optional) a design matrix. |
LOG |
logical; if TRUE, the log-marginal likelihood is returned. |
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.
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.
rmvSE
, dmvSE
.
# Generate Normally distributed data
require(mvtnorm)
y = rmvnorm(100, rep(2,2), diag(2))
# Marginal likelihood (exact value)
MNmargLike(y, X=NULL, LOG=TRUE)
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