Convergence bound in total variation for an image restoration model

Oliver Jovanovski

Statistics & Probability Letters, 2014, vol. 90, issue C, 11-16

Abstract: We consider a stochastic image restoration model proposed by A. Gibbs (2004), and give an upper bound on the time it takes for a Markov chain defined by this model to be ϵ-close in total variation to equilibrium. We use Gibbs’ result for convergence in the Wasserstein metric to arrive at our result. Our bound for the time to equilibrium of similar order to that of Gibbs.

Keywords: Markov chain; Gibbs sampler; MCMC; Image restoration (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016771521400100X
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:90:y:2014:i:c:p:11-16

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.2014.03.007

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 ().