Markov Chain Monte Carlo: an introduction for epidemiologists
Markov Chain Monte Carlo: an introduction for epidemiologists
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DOI：
10.1093/ije/dyt043
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发表时间：
2013-04-01
影响因子：
7.7
通讯作者：
Richardson, David
中科院分区：
文献类型：
--
作者：
Hamra, Ghassan;MacLehose, Richard;Richardson, David
马尔可夫链蒙特卡罗（MCMC）方法越来越受到流行病学家的欢迎。部分原因可能是MCMC提供了一种有吸引力的方法来处理一些困难的分析类型。此外，MCMC方法是贝叶斯分析中最常用的方法。然而，流行病学家在很大程度上仍然不熟悉MCMC。他们可能不熟悉MCMC的实现或对结果输出的解释。在过去的几十年里，有一些教程概述了最大似然背后的微积分，现在需要对MCMC的机制进行简单的描述。本文介绍了用MCMC进行分析的方法，并表明在给定相同的数据和一定的模型规范下，MCMC模拟的结果与基于标准最大似然估计（MLE）的方法的结果相匹配。此外，我们还重点介绍了一些实例，其中MCMC数据分析方法比MLE提供了明显的优势。我们希望这篇简短的教程将鼓励流行病学家考虑将MCMC方法作为其分析工具包的一部分。
Markov Chain Monte Carlo (MCMC) methods are increasingly popular among epidemiologists. The reason for this may in part be that MCMC offers an appealing approach to handling some difficult types of analyses. Additionally, MCMC methods are those most commonly used for Bayesian analysis. However, epidemiologists are still largely unfamiliar with MCMC. They may lack familiarity either with he implementation of MCMC or with interpretation of the resultant output. As with tutorials outlining the calculus behind maximum likelihood in previous decades, a simple description of the machinery of MCMC is needed. We provide an introduction to conducting analyses with MCMC, and show that, given the same data and under certain model specifications, the results of an MCMC simulation match those of methods based on standard maximum-likelihood estimation (MLE). In addition, we highlight examples of instances in which MCMC approaches to data analysis provide a clear advantage over MLE. We hope that this brief tutorial will encourage epidemiologists to consider MCMC approaches as part of their analytic tool-kit.