Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
Calculate Predictive Moments
Evaluate Predictive Densities
Generate Marginal Samples of Predictive Distribution
Generate Posterior Samples
Kernel Functions for Gaussian Processes
Log Predictive Density Score
Plot method for 1D marginal predictions
Plot method for 2D marginal predictions from shrinkGPR
Graphical summary of posterior of theta
Graphical summary of posterior of theta
Generate Predictions
Generate Predictions
Gaussian Process Regression with Shrinkage and Normalizing Flows
Student-t Process Regression with Shrinkage and Normalizing Flows
Simulate Data for Gaussian Process Regression
Sylvester Normalizing Flow
Efficient variational inference methods for fully Bayesian Gaussian Process Regression (GPR) models with hierarchical shrinkage priors, including the triple gamma prior for effective variable selection and covariance shrinkage in high-dimensional settings. The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.