sim_mniw: Generate Normal-IW Random Family

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Generate Normal-IW Random Family

Description

This function samples normal inverse-wishart matrices.

Usage

sim_mniw(num_sim, mat_mean, mat_scale_u, mat_scale, shape, u_prec = FALSE)

Arguments

num_sim	Number to generate
mat_mean	Mean matrix of MN
mat_scale_u	First scale matrix of MN
mat_scale	Scale matrix of IW
shape	Shape of IW
u_prec	If TRUE, use mat_scale_u as its inverse. By default, FALSE.

Details

Consider (Y_i, \Sigma_i) \sim MIW(M, U, \Psi, \nu).

1. Generate upper triangular factor of \Sigma_i = C_i C_i^T in the upper triangular Bartlett decomposition.

2. Standard normal generation: n x k matrix Z_i = [z_{ij} \sim N(0, 1)] in row-wise direction.

3. Lower triangular Cholesky decomposition: U = P P^T

4. A_i = M + P Z_i C_i^T

bvhar documentation built on April 4, 2025, 5:22 a.m.