SEMINARS
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Fall 2007
STATISTICS
COLLOQUIUM
Friday, November 9, 2007
12:30-1:30—Refreshments--Yost Hall, Room 101 (Refreshments)
Rong Chen, PhD
Department of Statistics, Rutgers University
Sequential Monte Carlo Methods and Their Applications: An Overview and Recent Developments
The sequential Monte Carlo (SMC) methodology recently 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, opening up new frontiers for cross-fertilization between statistical science and many
application areas.
SMC can be loosely defined as a family of techniques that use Monte Carlo simulations to solve on-line estimation and
prediction problems in stochastic dynamic systems. By recursively generating random samples of the state variables,
SMC adapts flexibly to the dynamics of the underlying stochastic systems. In this talk, we present an overview of
the current status of SMC, its applications and some recent developments. Specifically, we will introduce a general
framework of SMC, and discuss various strategies on fine-tuning the different components in the SMC algorithm,
in order to achieve maximum efficiency. SMC applications, specially those in science, engineering, bioinformatics
and financial data analysis will be discussed.
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