A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses
Methodology
2020-08-10 v1
Abstract
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.
Cite
@article{arxiv.2008.02926,
title = {A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses},
author = {M. S. Hamada and T. L. Graves and N. W. Hengartner and D. M. Higdon and A. V. Huzurbazar and E. C. Lawrence and C. D. Linkletter and C. S. Reese and D. W. Scott and R. R. Sitter and R. L. Warr and B. J. Williams},
journal= {arXiv preprint arXiv:2008.02926},
year = {2020}
}