Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference

We improve amortized Bayesian inference by inverting Bayes' theorem to estimate the implicit model evidence. We leverage the observation that deviations from constant evidence reveal approximation errors, which we penalize through a novel self-consistency loss. This loss improves inference quality in low-data regimes and boosts the training of neural density estimators across synthetic and real-world models.