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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 todoi:10.3758/s13423-016-1015-8 pmid:26968853 pmcid:PMC5862921 fatcat:3oqyp5bphvfcxocsvdqmf6jqsu