中国人民大学统计学院

报告题目

Sequential Monte Carlo Methods and Their Applications: An Overview and Recent Developments

报告人

Rong Chen

职务

• Department of Statistics,Rutgers University
• Department of Business Statistics and Econometrics,Peking University

摘要

The sequential Monte Carlo (SMC) methodology 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.

时间

2009年6月8日下午16：00

地点

明德主楼1016

欢迎各位老师同学参加