Bayesian Synthetic Likelihood
Abstract
Bayesian statistics is concerned with conducting posterior inference for the unknown quantities in a given statistical model. Conventional Bayesian inference requires the specification of a probabilistic model for the observed data, and the construction of the resulting likelihood function. However, sometimes the model is so complicated that evaluation of the likelihood is infeasible, which renders exact Bayesian inference impossible. Bayesian synthetic likelihood (BSL) is a posterior approximation procedure that can be used to conduct inference in situations where the likelihood is intractable, but where simulation from the model is straightforward. In this entry, we give a high-level presentation of BSL, and its extensions aimed at delivering scalable and robust posterior inferences.
Cite
@article{arxiv.2305.05120,
title = {Bayesian Synthetic Likelihood},
author = {David T. Frazier and Christopher Drovandi and David J. Nott},
journal= {arXiv preprint arXiv:2305.05120},
year = {2023}
}
Comments
This manuscript will eventually appear in Wiley StatsRef-Statistics Reference Online, and should not be confused with the original article on Bayesian Synthetic Likelihood by Price, Drovandi, Nott and Lee (2018)