学术报告报告题目：Parallel in time sampling for continuous time Markov chains with rare events
报 告 人：王挺 博士（University of Delaware）
报告时间：2018年10月9日（周二）下午4:30 -5:30
报告地点：4号楼4318室
邀 请人：何志坚 副教授
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数学学院
2018年9月25日
报告摘要：
Continuous time Markov chain (CTMC) is an important tool in applications such as modeling the stochastic reaction kinetics, queueing systems, etc. In this talk, we consider the problem of sampling the stationary distribution of ergodic CTMCs that exhibit multiple time scales. This is often computationally demanding due to the rare events associated with the slow time scale. On top of the parallel replica (ParRep) dynamics originally designed by Art Voter, we develop a parallel in time method for steady state sampling of CTMC. We provide theoretical analysis of ParRep using the theory of quasi-stationary distribution. If time permits, we will also discuss the parametric uncertainty quantification of CTMC. To conclude, the application of ParRep to several biological examples such as the genetic switch will be demonstrated.
报告人简介：
Ting Wang is a postdoc researcher in the Department of Mathematical Sciences at University of Delaware, working with Professor Petr Plechac on uncertainty quantification. His research mainly focuses on stochastic methods and uncertainty quantification for complex physical systems with an application in molecular dynamics simulations. He received his Ph.D from the University of Maryland Baltimore County under the supervision of Professor Muruhan Rathinam.