deepgp: Deep Gaussian Processes using MCMC

Performs posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. 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, 2020) and optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Covariance kernel options are matern (default) and squared exponential. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.

Package details

AuthorAnnie Sauer <anniees@vt.edu>
MaintainerAnnie Sauer <anniees@vt.edu>
LicenseLGPL
Version1.1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("deepgp")

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deepgp documentation built on Dec. 28, 2022, 1:32 a.m.