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. Applicable to both noisy and deterministic functions.
Incorporates SNOW parallelization and utilizes C and C++ under the hood.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.6) |
| Imports: |
grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, Matrix, Rcpp, mvtnorm, FNN |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
interp, knitr, rmarkdown |
| Published: |
2022-06-20 |
| Author: |
Annie Sauer |
| Maintainer: |
Annie Sauer <anniees at vt.edu> |
| License: |
LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
deepgp results |
Documentation:
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