CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914008358977536 |
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| author | Cabezas, Luben M. C. Santos, Vagner S. Ramos, Thiago R. Rodrigues, Pedro L. C. Izbicki, Rafael |
| author_facet | Cabezas, Luben M. C. Santos, Vagner S. Ramos, Thiago R. Rodrigues, Pedro L. C. Izbicki, Rafael |
| contents | Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods. However, posterior approximations obtained with SBI are often miscalibrated, causing credible regions to undercover true parameters. We develop $\texttt{CP4SBI}$, a model-agnostic conformal calibration framework that constructs credible sets with local Bayesian coverage. Our two proposed variants, namely local calibration via regression trees and CDF-based calibration, enable finite-sample local coverage guarantees for any scoring function, including HPD, symmetric, and quantile-based regions. Experiments on widely used SBI benchmarks demonstrate that our approach improves the quality of uncertainty quantification for neural posterior estimators using both normalizing flows and score-diffusion modeling. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_17077 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference Cabezas, Luben M. C. Santos, Vagner S. Ramos, Thiago R. Rodrigues, Pedro L. C. Izbicki, Rafael Machine Learning Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods. However, posterior approximations obtained with SBI are often miscalibrated, causing credible regions to undercover true parameters. We develop $\texttt{CP4SBI}$, a model-agnostic conformal calibration framework that constructs credible sets with local Bayesian coverage. Our two proposed variants, namely local calibration via regression trees and CDF-based calibration, enable finite-sample local coverage guarantees for any scoring function, including HPD, symmetric, and quantile-based regions. Experiments on widely used SBI benchmarks demonstrate that our approach improves the quality of uncertainty quantification for neural posterior estimators using both normalizing flows and score-diffusion modeling. |
| title | CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.17077 |