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.
|Author||Annie Sauer <firstname.lastname@example.org>|
|Maintainer||Annie Sauer <email@example.com>|
|Package repository||View on CRAN|
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