gen_posterior_samples: Generate Posterior Samples

In shrinkGPR: Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors

Generate Posterior Samples

Description

gen_posterior_samples generates posterior samples of the model parameters from a fitted shrinkGPR model.

Usage

gen_posterior_samples(mod, nsamp = 1000)

Arguments

mod	A shrinkGPR object representing the fitted Gaussian process regression model.
nsamp	Positive integer specifying the number of posterior samples to generate. Default is 1000.

Details

This function draws posterior samples from the latent space and transforms them into the parameter space of the model. These samples can be used for posterior inference or further analysis.

Value

A list containing posterior samples of the model parameters:

• thetas: A matrix of posterior samples for the inverse lengthscale parameters.

• sigma2: A matrix of posterior samples for the noise variance.

• lambda: A matrix of posterior samples for the global shrinkage parameter.

• betas (optional): A matrix of posterior samples for the mean equation parameters (if included in the model).

• lambda_mean (optional): A matrix of posterior samples for the mean equation's global shrinkage parameter (if included in the model).

Examples

if (torch::torch_is_installed()) {
  # Simulate data
  set.seed(123)
  torch::torch_manual_seed(123)
  n <- 100
  x <- matrix(runif(n * 2), n, 2)
  y <- sin(2 * pi * x[, 1]) + rnorm(n, sd = 0.1)
  data <- data.frame(y = y, x1 = x[, 1], x2 = x[, 2])

  # Fit GPR model
  res <- shrinkGPR(y ~ x1 + x2, data = data)

  # Generate posterior samples
  samps <- gen_posterior_samples(res, nsamp = 1000)

  # Plot the posterior samples
  boxplot(samps$thetas)
  }

shrinkGPR documentation built on April 4, 2025, 3:07 a.m.