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.

翻译：模型误设分析策略（如异常检测、模型验证和模型比较）是科学模型开发的关键组成部分。近年来，基于仿真的推断技术被越来越多地应用于复杂的前向模型中进行贝叶斯参数估计。然而，要构建完全基于仿真的分析流程，迫切需要建立一个全面的基于仿真的模型误设分析框架。本研究通过畸变驱动的模型误设检验，为广泛的模型差异分析任务提供了坚实而灵活的基础。从理论角度，我们引入了围绕对仿真模型进行多重畸变假设检验构建的统计框架，并明确建立了与经典技术（异常检测、模型验证及拟合优度残差分析）的解析联系。此外，我们提出了一种对实践者有用的高效自校准训练算法。通过在多种场景中展示该框架的性能，我们将其与经典结果在有效范围内建立关联。最后，我们演示了如何对真实引力波数据（特别是GW150914事件）实施此类畸变驱动的模型误设检验。