A simple introduction to Markov Chain Monte-Carlo sampling

被引：338
|
作者：
van Ravenzwaaij, Don [1 ,2 ]
Cassey, Pete [2 ]
Brown, Scott D. [2 ]
机构：
[1] Univ Groningen, Dept Psychol, Grote Kruisstr 2-1,Heymans Bldg,Room H169, NL-9712 TS Groningen, Netherlands
[2] Univ Newcastle, Dept Psychol, Univ Dr,Aviat Bldg, Callaghan, NSW 2308, Australia
来源：
PSYCHONOMIC BULLETIN & REVIEW | 2018年 / 25卷 / 01期
关键词：
Markov Chain Monte-Carlo; MCMC; Bayesian inference; Tutorial; GIBBS SAMPLER; MODEL; MEMORY; CHOICE;
D O I：
10.3758/s13423-016-1015-8
中图分类号：
B841 [心理学研究方法];
学科分类号：
040201 ;
摘要：
Markov Chain Monte-Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It describes what MCMC is, and what it can be used for, with simple illustrative examples. Highlighted are some of the benefits and limitations of MCMC sampling, as well as different approaches to circumventing the limitations most likely to trouble cognitive scientists.
引用
收藏
页码：143 / 154
页数：12
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