sample_post_t_ref_marg_mu: Metropolis-Hastings algorithm for the t-distribution and...

In BayesMultMeta: Bayesian Multivariate Meta-Analysis

Metropolis-Hastings algorithm for the t-distribution and Berger and Bernardo reference prior, where \mathbf{μ} is generated from the marginal posterior.

Description

This function implements Metropolis-Hastings algorithm for drawing samples from the posterior distribution of \mathbf{μ} and \mathbf{Ψ} under the assumption of the t-distribution when the Berger and Bernardo prior is employed. At each step, the algorithm starts with generating a draw from the marginal distribution of \mathbf{μ}.

Usage

sample_post_t_ref_marg_mu(X, U, d, Np)

Arguments

X	A p \times n matrix which contains n observation vectors of dimension p.
U	A p n \times p n block-diagonal matrix which contains the covariance matrices of observation vectors.
d	Degrees of freedom for the t-distribution
Np	Length of the generated Markov chain.

Value

List with the generated samples from the joint posterior distribution of \mathbf{μ} and \mathbf{Ψ}, where the values of \mathbf{Ψ} are presented by using the vec operator.

BayesMultMeta documentation built on June 9, 2022, 9:06 a.m.