Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference

arXiv:2310.04395v3 Announce Type: replace
Abstract: We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and data. In a nutshell, we invert Bayes' theorem and estimate the marginal likelihood based on approximate representations of the joint model. Upon perfect approximation, the marginal likelihood is constant across all parameter values by definition. However, errors in approximate inference lead to undesirable variance in the marginal likelihood estimates …

abstract accuracy arxiv bayes bayesian bayesian inference cs.ai cs.lg data efficiency inference likelihood parameters probabilistic model theorem type universal