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Activity Number: 505
Type: Contributed
Date/Time: Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #307596
Title: An Implementation of Constrained Sequential Monte Carlo by Particle Resizing
Author(s): Lixin Lang*+ and Prem K. Goel and Bhavik R. Bakshi
Companies: The Ohio State University and The Ohio State University and The Ohio State University
Address: 1958 Neil Ave., columbus, OH, 43210,
Keywords: sequential Monte Carlo ; particle filter ; effective sample size
Abstract:

The Sequential Monte Carlo (SMC) method, also known as particle filtering, has been successfully applied to perform Bayesian analysis in engineering problems. As a recursive simulation method, it allows rapid online estimation for high dimensional nonlinear dynamic models. However, it can give invalid results if ignoring some constraints, which are quite common in many chemical processes, say, a limited range of concentration degree. In the paper, we propose a particle resizing method to make an efficient implementation of SMC in such a problem. Simulation results are also given to demonstrate its performance.

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