A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses

A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses

This article presents a Bayesian inferential method where the likelihood for a model is unknown but where data can easily be simulated from the model. We discretize simulated (continuous) data to estimate the implicit likelihood in a Bayesian analysis employing a Markov chain Monte Carlo algorithm....

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Veröffentlicht in:arXiv.org 2020-08
Hauptverfasser: Hamada, M S, Graves, T L, Hengartner, N W, Higdon, D M, Huzurbazar, A V, Lawrence, E C, Linkletter, C D, Reese, C S, Scott, D W, Sitter, R R, Warr, R L, Williams, B J
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Bayesian analysis
Computer simulation
Markov chains
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Zusammenfassung:This article presents a Bayesian inferential method where the likelihood for a model is unknown but where data can easily be simulated from the model. We discretize simulated (continuous) data to estimate the implicit likelihood in a Bayesian analysis employing a Markov chain Monte Carlo algorithm. Three examples are presented as well as a small study on some of the method's properties.
ISSN:2331-8422