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Using simulation to handle implicit likelihoods in a Bayesian analysis
Quality Engineering ( IF 1.3 ) Pub Date : 2021-02-10 , DOI: 10.1080/08982112.2020.1814959 M. S. Hamada 1 , T. L. Graves 2 , N. W. Hengartner 1 , D. M. Higdon 3 , A. V. Huzurbazar 1 , E. Lawrence 1 , C. D. Linkletter 4 , C. S. Reese 5 , D. W. Scott 6 , R. R. Sitter 7 , R. L. Warr 5 , B. J. Williams 1
中文翻译：
使用模拟处理贝叶斯分析中的隐式似然
更新日期：2021-02-11
Quality Engineering ( IF 1.3 ) Pub Date : 2021-02-10 , DOI: 10.1080/08982112.2020.1814959 M. S. Hamada 1 , T. L. Graves 2 , N. W. Hengartner 1 , D. M. Higdon 3 , A. V. Huzurbazar 1 , E. Lawrence 1 , C. D. Linkletter 4 , C. S. Reese 5 , D. W. Scott 6 , R. R. Sitter 7 , R. L. Warr 5 , B. J. Williams 1
Affiliation
KEY POINTS
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.
中文翻译：
使用模拟处理贝叶斯分析中的隐式似然
关键点
本文提出了一种贝叶斯推理方法，其中模型的似然性未知，即隐式似然，但是可以轻松地从数据模型中模拟数据。我们使用模拟数据来估计采用马尔可夫链蒙特卡洛算法的贝叶斯分析中的隐式可能性。给出两个例子。
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