deepgp: Sequential Design for Deep Gaussian Processes using MCMC

Performs model fitting and sequential design for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. 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. Sequential design criteria include integrated mean-squared error (IMSE), active learning Cohn (ALC), and expected improvement (EI). Applicable to both noisy and deterministic functions. Incorporates SNOW parallelization and utilizes C and C++ under the hood.

Package details

AuthorAnnie Sauer <>
MaintainerAnnie Sauer <>
Package repositoryView on CRAN
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deepgp documentation built on Dec. 11, 2021, 9:22 a.m.