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March 11, 2016Psychonomic Bulletin & Review640 citationsOpen Access

A simple introduction to Markov Chain Monte–Carlo sampling

DRDon van RavenzwaaijPCPete CasseySBScott Brown

Key Points

  • To provide an introductory overview of Markov Chain Monte Carlo (MCMC) sampling and present practical solutions for common analytical challenges encountered in cognitive science.
  • Reviewed foundational concepts and mathematical principles underlying MCMC algorithms for estimating posterior probability distributions in Bayesian frameworks.
  • Analyzed conceptual examples to outline major computational benefits, operational bottlenecks, and implementation strategies for cognitive modeling.
  • Demonstrated how MCMC methods reliably approximate complex posterior distributions when analytical solutions are intractable.
  • Identified primary sampling limitations troubling cognitive research and provided methodological approaches to successfully navigate and resolve them.

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.

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Cite This Study

Ravenzwaaij et al. (2016) studied this question.

synapsesocial.com/papers/69d8105b3eff0c9dfaae32afhttps://doi.org/10.3758/s13423-016-1015-8
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