Preconditioned Neural Posterior Estimation for Likelihood-free Inference

arXiv:2404.13557v1 Announce Type: cross
Abstract: Simulation based inference (SBI) methods enable the estimation of posterior distributions when the likelihood function is intractable, but where model simulation is feasible. Popular neural approaches to SBI are the neural posterior estimator (NPE) and its sequential version (SNPE). These methods can outperform statistical SBI approaches such as approximate Bayesian computation (ABC), particularly for relatively small numbers of model simulations. However, we show in this paper that the NPE methods are not guaranteed to be …

abstract arxiv cs.lg estimator free function inference likelihood popular posterior simulation statistical stat.ml type