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A lognormal central limit theorem for particle approximations of normalizing constants

Dublin Core		PKP Metadata Items	Metadata for this Document
1.	Title	Title of document	A lognormal central limit theorem for particle approximations of normalizing constants
2.	Creator	Author's name, affiliation, country	Jean Bérard; University of Strasbourg; France
2.	Creator	Author's name, affiliation, country	Pierre Del Moral; University of New South Wales; Australia
2.	Creator	Author's name, affiliation, country	Arnaud Doucet; University of Oxford; United Kingdom
3.	Subject	Discipline(s)
3.	Subject	Keyword(s)	Feynman-Kac formulae
3.	Subject	Subject classification	65C35, 47D08, 60F05
4.	Description	Abstract	Feynman-Kac path integration models arise in a large variety of scientic disciplines including physics, chemistry and signal processing. Their mean eld particle interpretations, termed Diusion or Quantum Monte Carlo methods in physics and Sequential Monte Carlo or Particle Filters in statistics and applied probability, have found numerous applications as they allow to sample approximately from sequences of complex probability distributions and estimate their associated normalizing constants.This article focuses on the lognormal fuctuations of these normalizing constant estimates when both the time horizon n  and the number of particles N  go to innity in such a way that n/N tends to some number between 0 and 1. To the best of our knowledge, this is the first result of this type for mean field type interacting particle systems. We also discuss special classes of models, including particle absorption models in time-homogeneous environment and hidden Markov models in ergodic random environment, for which more explicit descriptions of the limiting bias and variance can be obtained.
5.	Publisher	Organizing agency, location
6.	Contributor	Sponsor(s)
7.	Date	(YYYY-MM-DD)	2014-10-07
8.	Type	Status & genre	Peer-reviewed Article
8.	Type	Type
9.	Format	File format	PDF
10.	Identifier	Uniform Resource Identifier	http://ejp.ejpecp.org/article/view/3428
10.	Identifier	Digital Object Identifier	10.1214/EJP.v19-3428
11.	Source	Journal/conference title; vol., no. (year)	Electronic Journal of Probability; Vol 19
12.	Language	English=en	en
14.	Coverage	Geo-spatial location, chronological period, research sample (gender, age, etc.)
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