Markov Chain Monte Carlo in Practice.
Markov Chain Monte Carlo in Practice.
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DOI：
10.1146/annurev-statistics-040220-090158
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发表时间：
2021-10
影响因子：
20.8
通讯作者：
Galin L. Jones;Qian Qin
中科院分区：
文献类型：
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作者：
Galin L. Jones;Qian Qin
马尔可夫链蒙特卡罗（MCMC）是一套重要的工具，用于估计现代应用中常见的概率分布特征。为了使MCMC模拟产生可靠的结果，它需要生成代表目标分布的观测值，并且它必须足够长，以便Monte Carlo估计的误差很小。我们审查的方法来评估模拟工作的可靠性，重点是那些最有用的实际相关的设置。这些方法的优点和缺点进行了讨论。在几个例子和详细的案例研究中说明了这些方法。《统计年度审查》第9卷的预计最终在线出版日期为2022年3月。请访问http://www.annualreviews.org/page/journal/pubdates了解修订后的估计数。
Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of probability distributions commonly encountered in modern applications. For MCMC simulation to produce reliable outcomes, it needs to generate observations representative of the target distribution, and it must be long enough so that the errors of Monte Carlo estimates are small. We review methods for assessing the reliability of the simulation effort, with an emphasis on those most useful in practically relevant settings. Both strengths and weaknesses of these methods are discussed. The methods are illustrated in several examples and in a detailed case study. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.