Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference

arXiv:2301.13368v2 Announce Type: replace-cross
Abstract: Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods. While traditional statistical approaches like approximate Bayesian computation and Bayesian synthetic likelihood have been studied under well-specified and misspecified settings, they often suffer from inefficiencies due to wasted model simulations. Neural approaches, such as sequential neural likelihood (SNL) avoid this wastage by utilising all model simulations to train a neural surrogate for the likelihood function. However, the performance of …

abstract arxiv bayesian computation cs.lg inference likelihood robust simulation simulations stat.co statistical stat.me stat.ml synthetic type