Gearing Gaussian process modeling and sequential design towards stochastic simulators
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915056261791744 |
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| author | Binois, Mickael Fadikar, Arindam Stevens, Abby |
| author_facet | Binois, Mickael Fadikar, Arindam Stevens, Abby |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07306 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gearing Gaussian process modeling and sequential design towards stochastic simulators Binois, Mickael Fadikar, Arindam Stevens, Abby Optimization and Control Machine Learning 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. |
| title | Gearing Gaussian process modeling and sequential design towards stochastic simulators |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2412.07306 |