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Optimal Markov chain Monte Carlo sampling
Author(s) -
Chen TingLi
Publication year - 2013
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.1265
Subject(s) - markov chain monte carlo , gibbs sampling , markov chain , metropolis–hastings algorithm , monte carlo method , estimator , slice sampling , sampling (signal processing) , computer science , mathematics , umbrella sampling , stochastic matrix , algorithm , statistics , bayesian probability , physics , filter (signal processing) , quantum mechanics , computer vision , molecular dynamics
This article is a review article on the optimal Markov chain Monte Carlo ( MCMC ) sampling. The focus is on homogeneous Markov chains. This article first reviews the problem of finding the optimal transition matrix, which is defined to minimize the asymptotic variance of MCMC estimators. The article later reviews the locally optimal sampler ( LOS ), an MCMC sampling that performs local updates based on the optimal transition matrix. We conducted a simulation study to compare the LOS with the Metropolis–Hastings and the Gibbs Sampler. The LOS was shown to provide an improved rate of convergence over these two most popular sampling schemes. The implementation of the LOS requires only minor modifications in existing Gibbs sampling code. WIREs Comput Stat 2013. doi: 10.1002/wics.1265 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)