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Paper detail

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. Three examples are presented as well as a small study on some of the method's properties.

preprint2020arXivOpen access
M. S. HamadaT. L. GravesN. W. HengartnerD. M. HigdonA. V. HuzurbazarE. C. LawrenceC. D. LinkletterC. S. ReeseD. W. ScottR. R. SitterR. L. WarrB. J. Williams
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M. S. HamadaT. L. GravesN. W. HengartnerD. M. HigdonA. V. HuzurbazarE. C. LawrenceC. D. LinkletterC. S. ReeseD. W. ScottR. R. SitterR. L. WarrB. J. Williams

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