JSM 2005 Online Program

Abstract #302673

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.

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Activity Number:	113
Type:	Invited
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Statistical Computing
Abstract - #302673
Title:	Sequential Monte Carlo: Past and Present
Author(s):	Jun S. Liu*+
Companies:	Harvard University
Address:	Department of Statistics, Cambridge, MA, 02138, USA
Keywords:	Importance sampling ; resampling ; particle filter ; protein folding ; target tracking
Abstract:	This talk will review historical developments in polymer simulation closely related to the popular Monte Carlo particle filtering, or more precisely, sequential Monte Carlo (SMC) methods. Two key elements in SMC are sequential importance sampling (SIS) and resampling. SIS is a general strategy for building up the trial distribution for a high-dimensional problem and can be applied naturally to accommodate dynamic systems and to mimic a learning mechanism. Because of SIS's sequential structure, one can monitor its importance sampling weights along with the sequential sampling and make appropriate interference, such as resampling and rejection sampling, to control Monte Carlo variations. SIS together with many interference techniques gives rise to a collection of related methods with the name "sequential Monte Carlo." We show some success stories of the method in energy minimization for protein folding, econometrics, target tracking, contingency table analysis, and digital telecommunications.

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