马尔可夫链蒙特卡罗的实践。

IF 21.4 1区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Annual Review of Public Health Pub Date : 2021-10-14 DOI:10.1146/annurev-statistics-040220-090158

Galin L. Jones, Qian Qin

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引用次数: 126

摘要

马尔可夫链蒙特卡罗（MCMC）是一组重要的工具，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，以便蒙特卡罗估计的误差较小。我们回顾了评估模拟工作可靠性的方法，重点是那些在实际相关环境中最有用的方法。讨论了这些方法的优点和缺点。这些方法在几个例子和一个详细的案例研究中进行了说明。《统计年鉴》第9卷预计最终在线出版日期为2022年3月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。

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Markov Chain Monte Carlo in Practice.

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.

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来源期刊

Annual Review of Public Health 医学-公共卫生、环境卫生与职业卫生

CiteScore

26.60

自引率

1.40%

发文量

审稿时长

>12 weeks

期刊介绍： The Annual Review of Public Health has been a trusted publication in the field since its inception in 1980. It provides comprehensive coverage of important advancements in various areas of public health, such as epidemiology, biostatistics, environmental health, occupational health, social environment and behavior, health services, as well as public health practice and policy. In an effort to make the valuable research and information more accessible, the current volume has undergone a transformation. Previously, access to the articles was restricted, but now they are available to everyone through the Annual Reviews' Subscribe to Open program. This open access approach ensures that the knowledge and insights shared in these articles can reach a wider audience. Additionally, all the published articles are licensed under a CC BY license, allowing users to freely use, distribute, and build upon the content, while giving appropriate credit to the original authors.