deepgp-package | R Documentation |
Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023). See Sauer (2023) for comprehensive methodological details and https://bitbucket.org/gramacylab/deepgp-ex/ for a variety of coding examples. 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 (2023). 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, 2022), and contour location through entropy (Booth, Renganathan, and Gramacy, 2024). 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.
fit_one_layer
: conducts MCMC sampling of
hyperparameters for a one layer GP
fit_two_layer
: conducts MCMC sampling of
hyperparameters and hidden layer for a two layer deep GP
fit_three_layer
: conducts MCMC sampling of
hyperparameters and hidden layers for a three layer deep GP
continue
: collects additional MCMC samples
trim
: cuts off burn-in and optionally thins
samples
predict
: calculates posterior mean and
variance over a set of input locations (optionally calculates EI or entropy)
plot
: produces trace plots, hidden layer
plots, and posterior predictive plots
ALC
: calculates active learning Cohn over
set of input locations using reference grid
IMSE
: calculates integrated mean-squared error
over set of input locations
Annie S. Booth annie_booth@ncsu.edu
Sauer, A. (2023). Deep Gaussian process surrogates for computer experiments.
*Ph.D. Dissertation, Department of Statistics, Virginia Polytechnic Institute and State University.*
http://hdl.handle.net/10919/114845
Sauer, A., Gramacy, R.B., & Higdon, D. (2023). Active learning for deep
Gaussian process surrogates. *Technometrics, 65,* 4-18. arXiv:2012.08015
Sauer, A., Cooper, A., & Gramacy, R. B. (2023). Vecchia-approximated deep Gaussian
processes for computer experiments.
*Journal of Computational and Graphical Statistics,* 1-14. arXiv:2204.02904
Gramacy, R. B., Sauer, A. & Wycoff, N. (2022). Triangulation candidates for Bayesian
optimization. *Advances in Neural Information Processing Systems (NeurIPS), 35,*
35933-35945. arXiv:2112.07457
Booth, A., Renganathan, S. A. & Gramacy, R. B. (2024). Contour location for
reliability in airfoil simulation experiments using deep Gaussian
processes. *In Review.* arXiv:2308.04420
# See "fit_one_layer", "fit_two_layer", "fit_three_layer",
# "ALC", or "IMSE" for examples
# Many more examples including real-world computer experiments are available at:
# https://bitbucket.org/gramacylab/deepgp-ex/
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