马尔可夫链蒙特卡罗采样简介。
A simple introduction to Markov Chain Monte-Carlo sampling.
机构信息
Department of Psychology, University of Groningen, Grote Kruisstraat 2/1, Heymans Building, room H169, Groningen, 9712TS, The Netherlands.
Department of Psychology, University of Newcastle, University Drive, Aviation Building, Callaghan, NSW, 2308, Australia.
出版信息
Psychon Bull Rev. 2018 Feb;25(1):143-154. doi: 10.3758/s13423-016-1015-8.
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.
马尔可夫链蒙特卡罗（MCMC）是一种越来越受欢迎的获取分布信息的方法，特别是在贝叶斯推断中估计后验分布。本文对 MCMC 采样进行了非常基本的介绍。它描述了 MCMC 是什么，以及它可以用简单的说明性示例来做什么。突出显示了 MCMC 采样的一些优点和局限性，以及克服最有可能困扰认知科学家的局限性的不同方法。
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