A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBI

2026-09-21

AI Summary

本文提出一种基于三向假设检验的框架，用于量化模拟基础推理中的认知校准不确定性，并评估必要的模拟预算。

Abstract

Current experimental scientists increasingly rely on simulation-based inference (SBI) to invert complex models with intractable likelihoods. A primary goal in these settings is to obtain credible regions with valid coverage. While recent model-agnostic conformal calibration methods have succeeded in constructing credible sets with prescribed local Bayesian coverage, their approximate nature introduces inherent epistemic uncertainty in the calibration process. In this work, we propose a novel tool for diagnosing calibration uncertainty. Our approach is based on a simple three-way hypothesis testing procedure. We demonstrate how this tool can be used to effectively assess necessary simulation budgets for calibration sets and analyze the epistemic uncertainty associated with cutoff estimation.

Problem

• epistemic uncertainty
• simulation-based inference
• conformal calibration

Innovation

• Three-Way Testing
• Epistemic Calibration Uncertainty
• Simulation-Based Inference