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数学学院、所系列学术报告(728场):台湾中央大学 傅承德教授

admin 发表于: 2017-09-04 08:52  点击:251

题  目 :Quickest Change Detection in Hidden Markov Models

报告人:台湾中央大学 傅承德教授

时  间:9月5日 上午10:00---12:00

地  点:数学楼一楼报告厅

摘  要:The quickest change detection problem is studied in sensor networks, where a set of sensors receive observations from a hidden Markov model (HMM) X and send sensor messages to a central processor, called the fusion center, which makes a final decision when observations are stopped. It is assumed that the parameter θ in the hidden Markov model for X changes from θ0 to θ1 at some unknown time. The problem is to determine the policies at the sensor and fusion center levels to jointly optimize the detection delay subject to the average run length (ARL) to false alarm constraint. In this talk, a CUSUM-type fusion rule with stationary bi- nary sensor messages is studied and a simple method for choosing the optimal local sensor thresholds is introduced. We also provide an efficient numerical computation method of the Kullback-Leibler information number for a two-state HMM. 

报告人简介:

    傅承德教授,现任台湾中央大学统计研究院教授,博士生导师。主要研究领域是高维数据的Boostrap和马氏链的理论研究方法和应用。在相关领域发表学术文章共70余篇。曾多次访问Stanford University、University of California at Berkeley、 Columbia University、University of Cambridge 和 University of Singapore 等知名大学。担任International Statistical Institute 理事,以及International Chinese Statistical Association、Chinese Probability and Statistical Association, Taiwan, R.O.C. 、Chinese Statistical Association, Taiwan, R.O.C.、National Statistical Honor Fraternity等学会的会员。曾任权威统计学杂志Statistica Sinica 的副主编、Journal of Data Science 副主编、Journal of the Chinese Statistical Association 主编和副主编。曾三次获得国家科学委员会的杰出项目研究奖项,还有中华概率统计协会杰出贡献奖等.