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Gearing Gaussian process modeling and sequential design towards stochastic simulators

arXiv 2412.07306 ↗First posted 10 Dec 2024math.OC stat.MLcited by 0

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Abstract (arXiv)

This chapter presents specific aspects of Gaussian process modeling in the presence of complex noise. Starting from the standard homoscedastic model, various generalizations from the literature are presented: input varying noise variance, non-Gaussian noise, or quantile modeling. These approaches are compared in terms of goal, data availability and inference procedure. A distinction is made between methods depending on their handling of repeated observations at the same location, also called replication. The chapter concludes with the corresponding adaptations of the sequential design procedures. These are illustrated in an example from epidemiology.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 93 references that are in the map. First posted on arXiv 10 Dec 2024.

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References · 31

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19 more
  1. 2018A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factorscited by 30
  2. 2017Replication or exploration? Sequential design for stochastic simulation experimentscited by 28
  3. 2021Bayesian Optimization over Hybrid Spacescited by 23
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  7. 2021GPflux: A Library for Deep Gaussian Processescited by 12
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  19. 2017Extended T-process Regression Modelscited by 1

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