sim_matgaussian: Generate Matrix Normal Random Matrix

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Generate Matrix Normal Random Matrix

Description

This function samples one matrix gaussian matrix.

Usage

sim_matgaussian(mat_mean, mat_scale_u, mat_scale_v, u_prec)

Arguments

mat_mean	Mean matrix
mat_scale_u	First scale matrix
mat_scale_v	Second scale matrix
u_prec	If TRUE, use mat_scale_u as its inverse.

Details

Consider n x k matrix Y_1, \ldots, Y_n \sim MN(M, U, V) where M is n x k, U is n x n, and V is k x k.

1. Lower triangular Cholesky decomposition: U = P P^T and V = L L^T

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

3. Y_i = M + P Z_i L^T

This function only generates one matrix, i.e. Y_1.

Value

One n x k matrix following MN distribution.

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