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。过去几年中，基于仿真的推断（SBI）技术在贝叶斯参数估计中的应用迅速增长，并被应用于日益复杂的前向模型。然而，为了迈向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。本工作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种畸变假设检验所构建的统计框架。我们还明确建立了与经典技术（异常检测、模型验证和拟合优度残差分析）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。我们在多种场景下展示了该框架的性能，并在经典结果有效时建立了与它们的联系。最后，我们展示了如何对真实的引力波数据（特别是事件GW150914）进行此类畸变驱动的模型误设检验。