{"title":"Markov Chain Monte Carlo in Practice.","authors":"Galin L. Jones, Qian Qin","doi":"10.1146/annurev-statistics-040220-090158","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":50752,"journal":{"name":"Annual Review of Public Health","volume":"1 1","pages":""},"PeriodicalIF":21.4000,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"126","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Public Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1146/annurev-statistics-040220-090158","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 126
Abstract
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.
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.
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