Constrained Sequential Monte Carlo
- 2010-06-03 (Thu.), 10:30 AM
- Auditorium, 2F, Tsai Yuan-Pei Memorial Hall
- Prof. Rong Chen
- Dept. of Statistics, Rutgers University, USA
Abstract
The sequential Monte Carlo (SMC) methodology has shown a great promise in solving a large class of highly complex inference and optimization problems. Originally it is designed to solve on-line filtering and smoothing problems for non-linear non-Gaussian state space models. Recently it has been shown to be equally powerful in dealing with fixed-dimensional problems, utilizing a sequential decomposition principle. In this talk we discuss issues and efficient implementations of SMC for dealing with a class of high dimensional distributions that are defined on restricted and ill-shaped spaces. Examples in bioinformatics and financial engineering are presented. ?
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