An Overview of Stochastic Approximation Monte Carlo. (9th June 2014)

• Record Type: Journal Article
• Authors: Liang, Faming
• Is Part Of: Wiley interdisciplinary reviews. Volume 6:Number 4(2014)
• Page Start: 240
• Page End: 254
• Publication Date: 2014-06-09
• Subjects: local trap -- MCMC -- parallel MCMC -- stochastic approximation Monte Carlo -- trajectory averaging; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1305
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : During the past few decades, Markov chain Monte Carlo (MCMC) has been widely used in Bayesian statistical inference and scientific computing. Its successes have proven it to be a very powerful and typically unique computational tool for analyzing data of complex structures. However, conventional MCMC algorithms often suffer from the local trap problem which renders the simulation ineffective. This paper provides an overview of the theory, variants, and applications for stochastic approximation Monte Carlo (SAMC), an advanced MCMC algorithm that is essentially immune to the local trap problem. WIREs Comput Stat 2014, 6:240–254. doi: 10.1002/wics.1305 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)