DMCA
Convergence of Sequential Monte Carlo Methods (2000)
Cached
Download Links
| Venue: | SEQUENTIAL MONTE CARLO METHODS IN PRACTICE |
| Citations: | 243 - 13 self |
BibTeX
@TECHREPORT{Crisan00convergenceof,
author = {Dan Crisan and Arnaud Doucet},
title = {Convergence of Sequential Monte Carlo Methods},
institution = {SEQUENTIAL MONTE CARLO METHODS IN PRACTICE},
year = {2000}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data arise in many applications in statistics and related fields. Recently, a large number of algorithms and applications based on sequential Monte Carlo methods (also known as particle filtering methods) have appeared in the literature to solve this class of problems; see (Doucet, de Freitas & Gordon, 2001) for a survey. However, few of these methods have been proved to converge rigorously. The purpose of this paper is to address this issue. We present a general sequential Monte Carlo (SMC) method which includes most of the important features present in current SMC methods. This method generalizes and encompasses many recent algorithms. Under mild regularity conditions, we obtain rigorous convergence results for this general SMC method and therefore give theoretical backing for the validity of all the algorithms that can be obtained as particular cases of it.
Keyphrases
sequential monte carlo method general smc method related field important feature many application many recent algorithm mild regularity condition general sequential monte carlo data arise current smc method bayesian estimation problem particular case freitas gordon posterior distribution large number rigorous convergence result give theoretical backing
