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

2020/08/07 by Michael S. Hamada, Timothy Graves, Hamada, M. S. +21
Mathematics · Computer Science · #Markov Chains and Monte Carlo Methods #Statistical Methods and Bayesian Inference #Gaussian Processes and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.2008.02926

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.

Citations

• Understanding the Metropolis-Hastings Algorithm

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