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 MetropolisHastings sampling of kernel hyperparameters. Vecchiaapproximation 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 


Author  Annie Sauer <anniees@vt.edu> 
Maintainer  Annie Sauer <anniees@vt.edu> 
License  LGPL 
Version  1.1.0 
Package repository  View on CRAN 
Installation 
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