<ul><li>Model misspecification analysis strategies, such as anomaly detection, model validation, and model comparison are a key component of scientific model development.</li><li>Simulation-based inference techniques are being used for Bayesian parameter estimation for complex forward models.</li><li>A comprehensive simulation-based framework for model misspecification analysis is needed.</li><li>The study introduces a statistical framework for performing hypothesis tests for distortions of the simulation model and demonstrates its performance in multiple scenarios.</li></ul>

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

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