lmn_marg: Marginal log-posterior for the LMN model.

In mlysy/LMN: Inference for Linear Models with Nuisance Parameters

Marginal log-posterior for the LMN model.

Description

Marginal log-posterior for the LMN model.

Usage

lmn_marg(suff, prior, post)

Arguments

suff	An object of class lmn_suff (see lmn_suff()).
prior	A list with elements Lambda, Omega, Psi, nu corresponding to the parameters of the prior MNIW distribution. See lmn_prior().
post	A list with elements Lambda, Omega, Psi, nu corresponding to the parameters of the posterior MNIW distribution. See lmn_post().

Value

The scalar value of the marginal log-posterior.

Examples

# generate data
n <- 50
q <- 2
p <- 3
Y <- matrix(rnorm(n*q),n,q) # response matrix
X <- matrix(rnorm(n*p),n,p) # covariate matrix
V <- .5 * exp(-(1:n)/n) # Toeplitz variance specification

suff <- lmn_suff(Y = Y, X = X, V = V, Vtype = "acf") # sufficient statistics
# default noninformative prior pi(Beta, Sigma) ~ |Sigma|^(-(q+1)/2)
prior <- lmn_prior(p = suff$p, q = suff$q)
post <- lmn_post(suff, prior = prior) # posterior MNIW parameters
lmn_marg(suff, prior = prior, post = post)

mlysy/LMN documentation built on March 25, 2022, 11:12 a.m.