提升仿真推断的不确定性量化精度，确保可信区间覆盖真实参数。
CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
- 提出无需依赖模型的局部校准框架，适配多种评分函数。
- 在有限样本下实现局部覆盖率保证，显著减少可信区间遗漏。
- 适合需要可靠不确定性估计的科研人员，尤其在复杂模型中使用。
当前实验科学家越来越多地依赖仿真推断（SBI）来反演具有不可解析似然的复杂非线性模型。然而，通过SBI获得的后验近似常出现校准偏差，导致可信区域无法覆盖真实参数。我们提出了\texttt{CP4SBI}，一种模型无关的合规模型校准框架，可构建具有局部贝叶斯覆盖率的可信集。提出的两种变体——基于回归树的局部校准与基于累积分布函数的校准——可为任意评分函数（包括HPD、对称及分位数基区域）提供有限样本下的局部覆盖率保证。在广泛使用的SBI基准测试上，该方法显著提升了使用归一化流与得分扩散建模的神经后验估计器的不确定性量化质量。
原文摘要 · Abstract (English)
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.
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