Analyzing Markov chain Monte Carlo output

被引：4

作者：

Vats, Dootika ^{[1
]}

Robertson, Nathan ^{[2
]}

Flegal, James M. ^{[2
]}

Jones, Galin L. ^{[3
]}

机构：

[1] Indian Inst Technol Kanpur, Dept Math & Stat, Kanpur, Uttar Pradesh, India

[2] Univ Calif Riverside, Dept Stat, Riverside, CA 92521 USA

[3] Univ Minnesota Twin Cities, Sch Stat, Minneapolis, MN USA

来源：

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS | 2020年 / 12卷 / 04期

基金：

美国国家科学基金会;

关键词：

Bayesian computation; Markov chain Monte Carlo; Monte Carlo; output analysis; stopping rules; INTRACTABLE PROBABILITY-DISTRIBUTIONS; SPECTRAL VARIANCE ESTIMATORS; GEOMETRIC ERGODICITY; CONVERGENCE-RATES; STRONG CONSISTENCY; GIBBS SAMPLERS; BATCH MEANS; HASTINGS; MCMC;

D O I：

10.1002/wics.1501

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Markov chain Monte Carlo (MCMC) is a sampling-based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sample from the desired distribution. As such it can be difficult to assess when the MCMC method is producing reliable results. We present some fundamental methods for ensuring a reliable simulation experiment. In particular, we present a workflow for output analysis in MCMC providing estimators, approximate sampling distributions, stopping rules, and visualization tools. This article is categorized under: Statistical Models > Bayesian Models Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods

引用

页数：12

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