AI RESEARCH
Flow Matching Calibration for Simulation-Based Inference under Model Misspecification
arXiv CS.LG
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ArXi:2509.23385v5 Announce Type: replace-cross Simulation-based inference (SBI) is transforming experimental sciences by enabling parameter estimation in complex non-linear models from simulated data. A persistent challenge, however, is model misspecification. In a Bayesian setting, targeting posterior distributions, errors may arise from the simulator, the noise or prior modelling. These model components are only approximations of reality, and severe mismatches can yield biased or overconfident posteriors. We address this issue by.