deepgp
Bayesian Deep Gaussian Processes using MCMC
Last update: 7 August 2023
Copyright license: GNU Library General Public License, version 2.0, GNU Lesser General Public License, version 2.1, GNU Lesser General Public License, version 3.0
Software version identifier: 1.1.0, 0.1.0, 0.2.0, 0.2.1, 0.3.0, 0.3.1, 1.0.0, 1.0.1, 1.1.1
Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <arXiv:2012.08015>). See Sauer (2023, <http://hdl.handle.net/10919/114845>) for comprehensive methodological details and <https://bitbucket.org/gramacylab/deepgp-ex/> 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.
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