sim_iw: Generate Inverse-Wishart Random Matrix

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Generate Inverse-Wishart Random Matrix

Description

This function samples one matrix IW matrix.

Usage

sim_iw(mat_scale, shape)

Arguments

mat_scale	Scale matrix
shape	Shape

Details

Consider \Sigma \sim IW(\Psi, \nu).

1. Upper triangular Bartlett decomposition: k x k matrix Q = [q_{ij}] upper triangular with

   1. q_{ii}^2 \chi_{\nu - i + 1}^2

   2. q_{ij} \sim N(0, 1) with i < j (upper triangular)

2. Lower triangular Cholesky decomposition: \Psi = L L^T

3. A = L (Q^{-1})^T

4. \Sigma = A A^T \sim IW(\Psi, \nu)

Value

One k x k matrix following IW distribution

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