A simple introduction to Markov Chain Monte–Carlo sampling

被引：0
|
作者：
Don van Ravenzwaaij
Pete Cassey
Scott D. Brown
机构：
[1] University of Groningen,Department of Psychology
[2] University of Newcastle,Department of Psychology
来源：
Psychonomic Bulletin & Review | 2018年 / 25卷
关键词：
Markov Chain Monte–Carlo; MCMC; Bayesian inference; Tutorial;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：143 / 154
页数：11
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