Accelerating reversible Markov chains

Ting-Li Chen and Chii-Ruey Hwang

Statistics & Probability Letters, 2013, vol. 83, issue 9, 1956-1962

Abstract: Reversibility is usually applied in most popular Markov chain Monte Carlo algorithms, such as the Metropolis–Hastings algorithm and the Gibbs sampler. However, several researchers have shown that non-reversible Markov chains are better than reversible ones. In this paper, we present a method for accelerating a reversible Markov chain. For any reversible Markov chain with a cycle on the corresponding graph, we construct a non-reversible Markov chain by adding some antisymmetric perturbations to the original chain. We prove that this non-reversible Markov chain is uniformly better than the original one in the sense of having a smaller asymptotic variance. Furthermore, we propose a conjecture that no uniformly better chain exists for the acyclic case.

Keywords: Markov chain Monte Carlo method; Rate of convergence; Reversibility; Asymptotic variance; Antisymmetric perturbation (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715213001533
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:83:y:2013:i:9:p:1956-1962

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2013.05.002

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().