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/deepgpex/> for a variety of coding examples. 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, 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.
Package details 


Author  Annie S. Booth <annie_booth@ncsu.edu> 
Maintainer  Annie S. Booth <annie_booth@ncsu.edu> 
License  LGPL 
Version  1.1.1 
Package repository  View on CRAN 
Installation 
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