Using simulation to handle implicit likelihoods in a Bayesian analysis. Issue Volume 33:Issues 1(2021) (24th December 2020)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Quality engineering. Volume 33:Issues 1(2021)
• Page Start: 172
• Page End: 177
• Publication Date: 2020-12-24
• Subjects: Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2020.1814959
• Languages: English
• ISSNs: 0898-2112

Abstract

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.