deepgp Package

Maintainer: Annie Sauer anniees@vt.edu

Performs model fitting and sequential design for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.
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. Sequential design criteria include integrated mean-squared error (IMSE), active learning Cohn (ALC), and expected improvement (EI). Applicable to both noisy and deterministic functions. Incorporates SNOW parallelization and utilizes C and C++ under the hood.

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

What’s new in version 0.3.0?

Reference

Sauer, A, RB Gramacy, and D Higdon. 2020. “Active Learning for Deep Gaussian Process Surrogates.” Technometrics, to appear; arXiv:2012.08015.