title: A simple introduction to Markov Chain Monte-Carlo sampling.
abstract: 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.
authors.affiliation: Department of Psychology, University of Groningen, Grote Kruisstraat 2/1, Heymans Building, room H169, Groningen, 9712TS, The Netherlands. d.van.ravenzwaaij@rug.nl.|Department of Psychology, University of Newcastle, University Drive, Aviation Building, Callaghan, NSW, 2308, Australia. d.van.ravenzwaaij@rug.nl.
authors.forename: Don
authors.initials: D
authors.lastname: van Ravenzwaaij
authors.affiliation: Department of Psychology, University of Newcastle, University Drive, Aviation Building, Callaghan, NSW, 2308, Australia.
authors.forename: Pete
authors.initials: P
authors.lastname: Cassey
authors.affiliation: Department of Psychology, University of Newcastle, University Drive, Aviation Building, Callaghan, NSW, 2308, Australia.
authors.forename: Scott D
authors.initials: SD
authors.lastname: Brown
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method
keywords: Bayesian inference; MCMC; Markov Chain Monte–Carlo; Tutorial