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Statistical identification of Markov chain
2017-12-07 10:54  

报告题目:Statistical identification of Markov chain

报 告  人:向绪言教授

报告时间:2017年12月8日16:00

报告地点:数计院307报告室

报告人简介:向绪言,博士,教授,硕士生导师,湖南文理学院 数学与计算科学学院院长;湖南省新世纪“121人才工程”人选;中国工程概率统计学会常务理事,湖南省数学会常务理事,湖南省侨联特聘专家委员会委员。主要从事随机过程统计、计算及应用(生物信息、神经网络、随机计算与智能系统)方向的科研工作,发表论文40余篇,20余篇被SCI、EI等收录。主持国家自然科学基金(面上项目)、教育部留学回国人员科研启动基金、湖南省自然科学基金等科研课题10余项,参与国家自然科学基金等10多项。

报告摘要:The theoretical study of continuous time homogeneous Markov chains is usually based a natural assumption on a known transition rate matrix (TRM). However TRM of a Markov chain in realistic systems might be unknown and even need be identified by partially observable data. Thus an issue how to identify the TRM of the underlying Mrakov chain by partially observable information is derived from the great significance in applications. That is what we call the statistical identification of Markov chain. Markov chain inversion approach has been derived for most of reversible Markov chains by partial observation at few states. Such approach has obvious advantages over others in that it can identify the most of reversible Markov chain without the requirement of bivariate distributions of subsequent sojourn time and hitting time, and in that the computation is accurate based on the accurate sojourn time PDFs and the prior information about the underlying topological structure of Markov chain. Hence the work opens up the possibility of carrying out the statistical identification for all reversible Markov chains.

 

数学与计算机科学学院
2017年12月7日
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