A simple introduction to Markov Chain Monte–Carlo sampling

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 us...

Descripción completa

Detalles Bibliográficos
Autores principales: van Ravenzwaaij, Don, Cassey, Pete, Brown, Scott D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Brief Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862921/
https://www.ncbi.nlm.nih.gov/pubmed/26968853
http://dx.doi.org/10.3758/s13423-016-1015-8
_version_ 1783308311385341952
author van Ravenzwaaij, Don
Cassey, Pete
Brown, Scott D.
author_facet van Ravenzwaaij, Don
Cassey, Pete
Brown, Scott D.
author_sort van Ravenzwaaij, Don
collection PubMed
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.
format Online
Article
Text
id pubmed-5862921
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-58629212018-03-28 A simple introduction to Markov Chain Monte–Carlo sampling van Ravenzwaaij, Don Cassey, Pete Brown, Scott D. Psychon Bull Rev Brief Report 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. Springer US 2016-03-11 2018 /pmc/articles/PMC5862921/ /pubmed/26968853 http://dx.doi.org/10.3758/s13423-016-1015-8 Text en © The Author(s) 2016 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (https://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.
spellingShingle Brief Report
van Ravenzwaaij, Don
Cassey, Pete
Brown, Scott D.
A simple introduction to Markov Chain Monte–Carlo sampling
title A simple introduction to Markov Chain Monte–Carlo sampling
title_full A simple introduction to Markov Chain Monte–Carlo sampling
title_fullStr A simple introduction to Markov Chain Monte–Carlo sampling
title_full_unstemmed A simple introduction to Markov Chain Monte–Carlo sampling
title_short A simple introduction to Markov Chain Monte–Carlo sampling
title_sort simple introduction to markov chain monte–carlo sampling
topic Brief Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862921/
https://www.ncbi.nlm.nih.gov/pubmed/26968853
http://dx.doi.org/10.3758/s13423-016-1015-8
work_keys_str_mv AT vanravenzwaaijdon asimpleintroductiontomarkovchainmontecarlosampling
AT casseypete asimpleintroductiontomarkovchainmontecarlosampling
AT brownscottd asimpleintroductiontomarkovchainmontecarlosampling
AT vanravenzwaaijdon simpleintroductiontomarkovchainmontecarlosampling
AT casseypete simpleintroductiontomarkovchainmontecarlosampling
AT brownscottd simpleintroductiontomarkovchainmontecarlosampling

Ejemplares similares