文章基本信息

标题：Using the Effective Sample Size as the Stopping Criterion in Markov Chain Monte Carlo with the Bayes Module in Mplus
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作者：Steffen Zitzmann ; Sebastian Weirich ; Martin Hecht 等
期刊名称：Psych
电子版ISSN：2624-8611
出版年度：2021
卷号：3
期号：3
页码：336-347
DOI：10.3390/psych3030025
语种：English
出版社：MDPI AG
摘要：Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation requires researchers to decide not only whether estimation has converged but also whether the Bayesian estimates are well-approximated by summary statistics from the chain. On the contrary, software such as the Bayes module in Mplus, which helps researchers check whether convergence has been achieved by comparing the potential scale reduction (PSR) with a prespecified maximum PSR, the size of the MCMC error or, equivalently, the effective sample size (ESS), is not monitored. Zitzmann and Hecht (2019) proposed a method that can be used to check whether a minimum ESS has been reached in Mplus. In this article, we evaluated this method with a computer simulation. Specifically, we fit a multilevel structural equation model to a large number of simulated data sets and compared different prespecified minimum ESS values with the actual (empirical) ESS values. The empirical values were approximately equal to or larger than the prespecified minimum ones, thus indicating the validity of the method.