This function implements Metropolis-Hastings algorithm for drawing samples from the posterior distribution of \(\mathbf{\mu}\) and \(\mathbf{\Psi}\) under the assumption of the normal distribution when the Berger and Bernardo reference prior is employed. At each step, the algorithm starts with generating a draw from the marginal distribution of \(\mathbf{\mu}\).
sample_post_nor_ref_marg_mu(X, U, Np)A \(p \times n\) matrix which contains \(n\) observation vectors of dimension \(p\).
A \(p n \times p n\) block-diagonal matrix which contains the covariance matrices of observation vectors.
Length of the generated Markov chain.
List with the generated samples from the joint posterior distribution of \(\mathbf{\mu}\) and \(\mathbf{\Psi}\), where the values of \(\mathbf{\Psi}\) are presented by using the vec operator.