提出基于扰动的模拟推断模型误设检测框架，提升复杂模型可信度。
Tests for model misspecification in simulation-based inference: from local distortions to global model checks
- 通过多重假设检验检测模拟模型的局部偏差
- 在引力波事件GW150914上验证了方法有效性
- 适合需要模型可信性分析的科研人员
模型误设分析（如异常检测、模型验证、拟合优度残差分析）是科学建模的关键环节。近年来，模拟推断（SBI）技术在贝叶斯参数估计中广泛应用，尤其适用于复杂前向模型。然而，要实现全模拟推断分析流程，亟需一套全面的模拟推断模型误设分析框架。本文提出一种基于扰动驱动的模型误设检测框架，从理论层面建立多个假设检验来识别模拟模型的偏差，并与经典方法（异常检测、模型验证、拟合优度分析）建立明确解析关联。同时引入一种高效自校准训练算法，便于实际应用。在多种场景中验证了该框架性能，并与经典结果保持一致。最后，将该方法应用于真实引力波数据，以事件GW150914为例进行测试。
原文摘要 · Abstract (English)
Model misspecification analysis strategies, such as anomaly detection, model validation, and model comparison are a key component of scientific model development. Over the last few years, there has been a rapid rise in the use of simulation-based inference (SBI) techniques for Bayesian parameter estimation, applied to increasingly complex forward models. To move towards fully simulation-based analysis pipelines, however, there is an urgent need for a comprehensive simulation-based framework for model misspecification analysis. In this work, we provide a solid and flexible foundation for a wide range of model discrepancy analysis tasks, using distortion-driven model misspecification tests. From a theoretical perspective, we introduce the statistical framework built around performing many hypothesis tests for distortions of the simulation model. We also make explicit analytic connections to classical techniques: anomaly detection, model validation, and goodness-of-fit residual analysis. Furthermore, we introduce an efficient self-calibrating training algorithm that is useful for practitioners. We demonstrate the performance of the framework in multiple scenarios, making the connection to classical results where they are valid. Finally, we show how to conduct such a distortion-driven model misspecification test for real gravitational wave data, specifically on the event GW150914.
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