Gibbs sampler for Conjugate Bayesian Multivariate Linear Models
bayesMvLMconjugate.RdGibbs sampler for Conjugate Bayesian Multivariate Linear Models
Arguments
- Y
matrix \(n \times q\) of response variables
- X
matrix \(n \times p\) of predictors
- mu_B
matrix \(p \times q\) prior mean for \(\beta\)
- V_B
matrix \(p \times p\) prior row covariance for \(\beta\)
- nu
double prior parameter for \(\Sigma\)
- Psi
matrix prior parameter for \(\Sigma\)
- n_iter
integer iteration number for Gibbs sampler
- burn_in
integer number of burn-in iteration
Value
B_samples array of posterior sample for \(\beta\)
Sigma_samples array of posterior samples for \(\Sigma\)
Examples
# \donttest{
## Generate data
n <- 100
p <- 3
q <- 2
Y <- matrix(rnorm(n*q), nrow = n, ncol = q)
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
## Prior parameters
mu_B <- matrix(0, p, q)
V_B <- diag(10, p)
nu <- 3
Psi <- diag(q)
## Samples from posteriors
n_iter <- 1000
burn_in <- 500
set.seed(1234)
samples <- spBPS::bayesMvLMconjugate(Y, X, mu_B, V_B, nu, Psi, n_iter, burn_in)
#>
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# }