Surrogate-Based Bayesian Inference: Uncertainty Quantification and Active Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908885763227648 |
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| author | Roberts, Andrew Gerard Dietze, Michael C. Huggins, Jonathan H. |
| author_facet | Roberts, Andrew Gerard Dietze, Michael C. Huggins, Jonathan H. |
| contents | Surrogate models - also called emulators - are widely used to facilitate Bayesian inference in settings where computational costs preclude the use of standard posterior inference algorithms. Their deployment is now standard practice across many scientific domains. However, integrating surrogates in statistical analyses introduces unique challenges that complicate established Bayesian workflow principles. While significant progress has been made in addressing these issues, the relevant developments are scattered across several distinct research communities, with different emphases and perspective. We present a unifying review that synthesizes the literature into a coherent framework, aiming to benefit both practitioners and methods developers. We place particular emphasis on propagating surrogate uncertainty and sequentially refining emulators via active learning, two key components of a robust surrogate-based Bayesian workflow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13646 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Surrogate-Based Bayesian Inference: Uncertainty Quantification and Active Learning Roberts, Andrew Gerard Dietze, Michael C. Huggins, Jonathan H. Methodology 62F15, 65C60 (Primary) 65C05 (Secondary) Surrogate models - also called emulators - are widely used to facilitate Bayesian inference in settings where computational costs preclude the use of standard posterior inference algorithms. Their deployment is now standard practice across many scientific domains. However, integrating surrogates in statistical analyses introduces unique challenges that complicate established Bayesian workflow principles. While significant progress has been made in addressing these issues, the relevant developments are scattered across several distinct research communities, with different emphases and perspective. We present a unifying review that synthesizes the literature into a coherent framework, aiming to benefit both practitioners and methods developers. We place particular emphasis on propagating surrogate uncertainty and sequentially refining emulators via active learning, two key components of a robust surrogate-based Bayesian workflow. |
| title | Surrogate-Based Bayesian Inference: Uncertainty Quantification and Active Learning |
| topic | Methodology 62F15, 65C60 (Primary) 65C05 (Secondary) |
| url | https://arxiv.org/abs/2603.13646 |