Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation

arXiv:2601.22367v2 Announce Type: replace-cross Abstract: Generalized Bayesian Inference (GBI) tempers a loss with a temperature $\beta > 0$ to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-based samplers and must be re-run for each new dataset and each $\beta$ value. We give the first fully amortized variational approximation for the tempered posterior family by training a single data- and $\beta$-conditioned neural posterior estimator that enables sampling in a single forward pass, without simulator

Source: arXiv cs.LG — read the full report at the original publisher.

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