A variational neural Bayes framework for inference on intractable posterior distributions

arXiv:2404.10899v1 Announce Type: cross
Abstract: Classic Bayesian methods with complex models are frequently infeasible due to an intractable likelihood. Simulation-based inference methods, such as Approximate Bayesian Computing (ABC), calculate posteriors without accessing a likelihood function by leveraging the fact that data can be quickly simulated from the model, but converge slowly and/or poorly in high-dimensional settings. In this paper, we propose a framework for Bayesian posterior estimation by mapping data to posteriors of parameters using a neural network trained on …

abstract arxiv bayes bayesian computing data framework function inference likelihood posterior simulation stat.co stat.ml type