A simple introduction to Markov Chain Monte–Carlo sampling

Don van Ravenzwaaij, Pete Cassey, Scott D. Brown
2016 Psychonomic Bulletin & Review  
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
more » ... e cognitive scientists. Keywords Markov Chain Monte-Carlo · MCMC · Bayesian inference · Tutorial Over the course of the twenty-first century, the use of Markov chain Monte-Carlo sampling, or MCMC, has grown dramatically. But, what exactly is MCMC? And why is its popularity growing so rapidly? There are many other tutorial articles that address these questions, and provide excellent introductions to MCMC. The aim of this article is not to replicate these, but to provide a more basic introduction that should be accessible for even very beginning Don van Ravenzwaaij
doi:10.3758/s13423-016-1015-8 pmid:26968853 pmcid:PMC5862921 fatcat:3oqyp5bphvfcxocsvdqmf6jqsu