JSM 2005 Online Program

Abstract #303876

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	476
Type:	Topic Contributed
Date/Time:	Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303876
Title:	Sequential Monte Carlo Methods for Static Problems
Author(s):	Arnaud Doucet*+
Companies:	Cambridge University
Address:	Signal Processing, Dept. of Engineering, Cambridge, International, CB3 ODS, United Kingdom
Keywords:	Sequential Monte Carlo ; Markov chain Monte Carlo ; Ratio of Normalizing constants ; Bayesian Estimation ; Bayes Factor
Abstract:	Sequential Monte Carlo (SMC) methods have been developed in the context of one interested in sampling from a sequence of distributions whose dimension is growing over time. We present a general SMC methodology to sample from a sequence of probability distributions defined on a common space (i.e., in cases where one traditionally uses Markov chain Monte Carlo [MCMC]). We propose to approximate these distributions by a large set of random samples, which evolves over time using simple sampling and resampling mechanisms. This methodology not only yields a whole set of principled algorithms to make parallel MCMC runs interact, but also provides new algorithms for sequential Bayesian estimation and global optimization. Additionally, new identities are obtained to estimate ratio of normalizing constants. This talk is illustrated by several examples arising in Bayesian inference. This is joint work with Pierre Del Moral and Gareth W. Peters.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program