Description
Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors.
Description
Efficient variational inference methods for fully Bayesian univariate and multivariate Gaussian and t-process regression models. Hierarchical shrinkage priors, including the triple gamma prior, are used 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>.