Using simulation to handle implicit likelihoods in a Bayesian analysis. Issue Volume 33:Issues 1(2021) (24th December 2020) - Hamada, M. S.; Graves, T. L.; Hengartner, N. W.; Higdon, D. M.; Huzurbazar, A. V.; Lawrence, E.; Linkletter, C. D.; Reese, C. S.; Scott, D. W.; Sitter, R. R.; Warr, R. L.; Williams, B. J.
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.