A simple introduction to Markov Chain Monte–Carlo sampling

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.

• Publication year

  2016

• Venue

  Psychonomic Bulletin & Review

• Publication date

  2016-03-11

• Fields of study

  Mathematics, Medicine, Psychology

• Identifiers
  DOI 10.3758/s13423-016-1015-8PMID 26968853PMCID 5862921
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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