- Title
- A simple introduction to Markov Chain Monte-Carlo sampling
- Creator
- van Ravenzwaaij, Don; Cassey, Pete; Brown, Scott D.
- Relation
- Journal of Mathematical Psychology Vol. 25, Issue 1, p. 143-154
- Publisher Link
- http://dx.doi.org/10.3758/s13423-016-1015-8
- Publisher
- Academic Press
- Resource Type
- journal article
- Date
- 2018
- Description
- 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.
- Subject
- Markov Chain Monte–Carlo; MCMC; Bayesian inference; tutorial
- Identifier
- http://hdl.handle.net/1959.13/1401383
- Identifier
- uon:34897
- Identifier
- ISSN:0022-2496
- Rights
- This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Language
- eng
- Full Text
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