sim_mnormal: Generate Multivariate Normal Random Vector

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Generate Multivariate Normal Random Vector

Description

This function samples n x muti-dimensional normal random matrix.

Usage

sim_mnormal(
  num_sim,
  mu = rep(0, 5),
  sig = diag(5),
  method = c("eigen", "chol")
)

Arguments

num_sim	Number to generate process
mu	Mean vector
sig	Variance matrix
method	Method to compute \Sigma^{1/2}. Choose between eigen (spectral decomposition) and chol (cholesky decomposition). By default, eigen.

Details

Consider x_1, \ldots, x_n \sim N_m (\mu, \Sigma).

1. Lower triangular Cholesky decomposition: \Sigma = L L^T

2. Standard normal generation: Z_{i1}, Z_{in} \stackrel{iid}{\sim} N(0, 1)

3. Z_i = (Z_{i1}, \ldots, Z_{in})^T

4. X_i = L Z_i + \mu

Value

T x k matrix

bvhar documentation built on June 25, 2025, 9:07 a.m.