deepgp: Bayesian Deep Gaussian Processes using MCMC

Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <arXiv:2012.08015>). 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 (2022, <arXiv:2204.02904>). 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, 2021 <arXiv:2112.07457>), and contour location through entropy (Sauer, 2023). 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.

Package details

AuthorAnnie S. Booth <>
MaintainerAnnie S. Booth <>
Package repositoryView on CRAN
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deepgp documentation built on Aug. 8, 2023, 1:06 a.m.