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Quality Quandaries: Using simulation to handle implicit likelihoods in a Bayesian analysis
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Quality Quandaries: Using simulation to handle implicit likelihoods in a Bayesian analysis

Publication:
Quality Engineering
Date:
February 2021
Issue:
Volume 33 Issue 1
Pages:
pp. 172-177
Author(s):
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.
The copyright of this article is not held by ASQ.

Abstract

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.

Keywords

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