pred_bayesMvLMconjugate: Predictive sampler for Conjugate Bayesian Multivariate Linear...

In spBPS: Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning

Predictive sampler for Conjugate Bayesian Multivariate Linear Models

Description

Predictive sampler for Conjugate Bayesian Multivariate Linear Models

Usage

pred_bayesMvLMconjugate(X_new, B_samples, Sigma_samples)

Arguments

X_new	matrix n_new \times p of predictors for new data points
B_samples	array of posterior sample for \beta
Sigma_samples	array of posterior samples for \Sigma

Value

Y_pred matrix of posterior mean for response matrix Y predictions

Y_pred_samples array of posterior predictive sample for response matrix Y

Examples

## 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)

## Extract posterior samples
B_samples <- samples$B_samples
Sigma_samples <- samples$Sigma_samples

## Samples from predictive posterior (based posterior samples)
m <- 50
X_new <- matrix(rnorm(m*p), nrow = m, ncol = p)
pred <- spBPS::pred_bayesMvLMconjugate(X_new, B_samples, Sigma_samples)

spBPS documentation built on Oct. 25, 2024, 5:07 p.m.