All-in-one simulation-based inference

arXiv:2404.09636v1 Announce Type: new
Abstract: Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amortized inference methods are simulation-hungry and inflexible: They require the specification of a fixed parametric prior, simulator, and inference tasks ahead of time. Here, we present a new amortized inference method -- the Simformer -- which overcomes these limitations. By training a probabilistic diffusion …

abstract arxiv bayesian bayesian inference cs.ai cs.lg current data however inference making networks neural networks parametric prior simulation simulations simulator solve stat.ml stochastic tasks trains type