deepgp

Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, doi:10.48550/arXiv.2012.08015). See Sauer (2023, http://hdl.handle.net/10919/114845) 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, doi:10.48550/arXiv.2204.02904). Optional monotonic warpings are implemented following Barnett et al. (2024, doi:10.48550/arXiv.2408.01540). 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 doi:10.48550/arXiv.2112.07457), and contour location through entropy (Booth, Renganathan, and Gramacy, 2024 doi:10.48550/arXiv.2308.04420). 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.

Last 30 days

This package has been downloaded 337 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 14 times.

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Last 365 days

This package has been downloaded 6,160 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was May 15, 2024 with 74 downloads.

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