标题： CP4SBI：基于仿真推断的可信集局部共形校准 显示英文标题

标题： CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference

摘要： 当前实验科学家越来越多地依赖基于仿真的推断（SBI）来反转具有不可处理似然的复杂非线性模型。 然而，通过SBI获得的后验近似通常校准不当，导致可信区域无法覆盖真实参数。 我们开发了$\texttt{CP4SBI}$，一种与模型无关的共形校准框架，该框架构建具有局部贝叶斯覆盖的可信集。 我们提出的两种变体，即通过回归树进行局部校准和基于CDF的校准，使得任何评分函数（包括HPD、对称和基于分位数的区域）都能获得有限样本的局部覆盖保证。 在广泛使用的SBI基准上的实验表明，我们的方法提高了使用归一化流和分数扩散建模的神经后验估计器的不确定性量化质量。 显示英文摘要

摘要： 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.