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On the efficiency of adaptive MCMC algorithms

Dublin Core		PKP Metadata Items	Metadata for this Document
1.	Title	Title of document	On the efficiency of adaptive MCMC algorithms
2.	Creator	Author's name, affiliation, country	Christophe Andrieu; University of Bristol
2.	Creator	Author's name, affiliation, country	Yves Atchade; University of Michigan
3.	Subject	Discipline(s)
3.	Subject	Keyword(s)
4.	Description	Abstract	We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an ``optimal'' target process via a learning procedure. We show, under appropriate conditions, that the adaptive MCMC chain and the ``optimal'' (nonadaptive) MCMC process share many asymptotic properties. The special case of adaptive MCMC algorithms governed by stochastic approximation is considered in details and we apply our results to the adaptive Metropolis algorithm of [Haario, Saksman, Tamminen].
5.	Publisher	Organizing agency, location
6.	Contributor	Sponsor(s)
7.	Date	(YYYY-MM-DD)	2007-10-12
8.	Type	Status & genre	Peer-reviewed Article
8.	Type	Type
9.	Format	File format	PDF
10.	Identifier	Uniform Resource Identifier	http://ecp.ejpecp.org/article/view/1320
10.	Identifier	Digital Object Identifier	10.1214/ECP.v12-1320
11.	Source	Journal/conference title; vol., no. (year)	Electronic Communications in Probability; Vol 12
12.	Language	English=en
14.	Coverage	Geo-spatial location, chronological period, research sample (gender, age, etc.)
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