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...

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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
Descripción
Sumario: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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