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Nonstationary Gaussian Process Surrogates

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arxiv 2305.19242 v2 pith:2CDV6QIP submitted 2023-05-30 stat.ME

classification stat.ME
keywords gaussiannonstationaryprocessesadaptationsavailablebake-offcodecomputer
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We provide a survey of nonstationary surrogate models which utilize Gaussian processes (GPs) or variations thereof, including nonstationary kernel adaptations, partition and local GPs, and spatial warpings through deep Gaussian processes. We also overview publicly available software implementations and conclude with a bake-off involving an 8-dimensional satellite drag computer experiment. Code for this example is provided in a public git repository.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  3. Gradient-enhancement and Gradient Predictions for Deep Gaussian Process Modeling of Expensive Computer Experiments

    stat.ME 2025-12 conditional novelty 6.0 of 10

    Gradient-enhanced deep Gaussian processes, built by sampling latent warpings and their derivatives via MCMC with a chain rule, outperform gradient-enhanced GPs and standard DGPs on nonstationary test functions.

  4. Active Learning via Heteroskedastic Rational Kriging

    stat.ME 2025-07 conditional novelty 6.0 of 10

    Heteroskedastic rational kriging is a fast, data-driven variance extension of rational kriging that improves active learning for computer experiments.

  5. Modernizing full posterior inference for surrogate modeling of categorical-output simulation experiments

    stat.CO 2025-01 conditional novelty 6.0 of 10

    A fully Bayesian GP classifier built from elliptical slice sampling and Vecchia approximation matches or beats variational baselines in log score on large benchmark and black-hole simulation problems.

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