deepgp: Bayesian Deep Gaussian Processes using MCMC
Annie S. Booth · 2020
Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, ). See Sauer (2023, ) for comprehensive methodological details and for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Vecchia-approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, ). Optional monotonic warpings are implemented following Barnett et al. (2024, ). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022 ), and contour location through entropy (Booth, Renganathan, and Gramacy, 2024 ). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.