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Analyzing Markov chain Monte Carlo output
Author(s) -
Vats Dootika,
Robertson Nathan,
Flegal James M.,
Jones Galin L.
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1501
Subject(s) - markov chain monte carlo , computer science , monte carlo method , bayesian probability , estimator , sampling (signal processing) , hybrid monte carlo , slice sampling , algorithm , statistics , mathematics , artificial intelligence , filter (signal processing) , computer vision
Abstract Markov chain Monte Carlo (MCMC) is a sampling‐based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sample from the desired distribution. As such it can be difficult to assess when the MCMC method is producing reliable results. We present some fundamental methods for ensuring a reliable simulation experiment. In particular, we present a workflow for output analysis in MCMC providing estimators, approximate sampling distributions, stopping rules, and visualization tools. This article is categorized under: Statistical Models > Bayesian Models Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods