Towards Reliable Simulation-based Inference
This work addresses the issue of overconfidence in statistical inference arising from machine learning approximations in scientific simulations, which can compromise result reliability. To mitigate this, the authors propose two complementary approaches: first, a “balanced” regularization strategy that explicitly suppresses model overconfidence; and second, a simulation-aware Bayesian neural network prior that naturally alleviates overconfidence without additional regularization, even in small-sample regimes. By integrating neural ratio estimation with uncertainty quantification techniques, the proposed methods significantly improve inference calibration, yielding posterior estimates that are either closer to the ground truth or conservatively biased. This enhanced calibration strengthens the credibility of simulation-based inference in scientific applications.