期刊
PSYCHONOMIC BULLETIN & REVIEW
卷 25, 期 1, 页码 143-154出版社
SPRINGER
DOI: 10.3758/s13423-016-1015-8
关键词
Markov Chain Monte-Carlo; MCMC; Bayesian inference; Tutorial
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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