JSM 2005 - Toronto

Abstract #302462

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 471
Type: Invited
Date/Time: Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #302462
Title: Sequential Monte Carlo Methods and Protein Structures
Author(s): Rong Chen*+
Companies: University of Illinois, Chicago
Address: Dept. Information and Decision Sciences (MC 294), Chicago, IL, 60607,
Keywords: Sequentail Monte Carlo ; Protein Structure ; Growth Principle
Abstract:

The Sequential Monte Carlo (SMC) methodology that has emerged in the fields of statistics and engineering has shown a great promise in solving a large class of highly complex inference and optimization problems. It is a family of Monte Carlo techniques based on the "growth principle" in dealing with high-dimensional complex systems. Specially, a high dimensional problem is decomposed into a sequence of low dimensional problems, which forms a stochastic dynamic system. Weighted Monte Carlo samples are drawn from each of simpler components, and their combination forms a Monte Carlo sample of the original system. In this talk, we present SMC approaches in studying geometric properties of proteins. Specifically, we introduce the general strategy of intermediate distribution design, look-ahead, multi-grid sampling, and constraint enforcement.

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Revised March 2005