<ul><li>This study presents a simulation-based framework for model misspecification analysis in the context of simulation-based inference (SBI) techniques for Bayesian parameter estimation.</li><li>The framework includes distortion-driven model misspecification tests and connections to classical techniques such as anomaly detection, model validation, and goodness-of-fit residual analysis.</li><li>An efficient self-calibrating training algorithm is introduced to improve the performance of the framework.</li><li>The framework is demonstrated in various scenarios and applied to real gravitational wave data (GW150914).</li></ul>

Tests for model misspecification in simulation-based inference: from local distortions to global model checks

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