arXiv:2508.17077v3 Announce Type: replace-cross 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, enabl
The increasing reliance on complex AI models in scientific research necessitates more robust methods for ensuring the reliability and interpretability of their outputs.
This development addresses a critical weakness in simulation-based inference (SBI): the miscalibration of posterior approximations, which erodes trust in AI-driven scientific discovery.
The introduction of CP4SBI provides a model-agnostic framework to produce credible sets with local Bayesian coverage, significantly improving the calibration and trustworthiness of AI-generated insights in science.
- · Experimental Scientists
- · AI/ML Researchers
- · Scientific Software Developers
- · Industries relying on complex simulations
- · Researchers using uncalibrated SBI methods
- · Hypothesis testing relying on miscalibrated credible regions
Improved reliability and reproducibility of scientific research employing AI-driven simulation-based inference.
Accelerated adoption of AI in scientific discovery across various disciplines due to enhanced trust in results.
Reduced time and cost for experimental validation as computational predictions become more accurate and trustworthy.
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