sim_mniw: Generate Normal-IW Random Family
In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

sim_mniw

R Documentation

Generate Normal-IW Random Family

This function samples normal inverse-wishart matrices.

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

`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`.

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

Generate upper triangular factor of \Sigma_i = C_i C_i^T in the upper triangular Bartlett decomposition.
Standard normal generation: n x k matrix Z_i = [z_{ij} \sim N(0, 1)] in row-wise direction.
Lower triangular Cholesky decomposition: U = P P^T
A_i = M + P Z_i C_i^T