{"ID":2879167,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17077","arxiv_id":"2508.17077","title":"CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference","abstract":"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.","short_abstract":"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{CP...","url_abs":"https://arxiv.org/abs/2508.17077","url_pdf":"https://arxiv.org/pdf/2508.17077v2","authors":"[\"Luben M. C. Cabezas\",\"Vagner S. Santos\",\"Thiago R. Ramos\",\"Pedro L. C. Rodrigues\",\"Rafael Izbicki\"]","published":"2025-08-23T16:13:10Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}