Using simulation to handle implicit likelihoods in a Bayesian analysis. Issue Volume 33:Issues 1(2021) (24th December 2020) - Hamada, M. S.
KEY POINTS: This article presents a Bayesian inferential method where the likelihood for a model is unknown, i.e., an implicit likelihood, but where data can easily be simulated from the data model. We use simulated data to estimate the implicit likelihood in a Bayesian analysis employing a Markov chain Monte Carlo algorithm. Two examples are presented.