deepgp Package

Maintainer: Annie Sauer

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; utilizes C and C++ with OpenMP parallelization under the hood.

Run help("deepgp-package") or help(package = "deepgp") for more information.


Sauer, A., Gramacy, R.B., & Higdon, D. (2020). Active learning for deep Gaussian process surrogates. Technometrics, (just-accepted), 1-39.

Sauer, A., Cooper, A., & Gramacy, R. B. (2022). Vecchia-approximated deep Gaussian processes for computer experiments. pre-print on arXiv:2204.02904

Version History

What’s new in version 1.0.0?

What’s new in version 0.3.0?