报告人：Wang Liangliang (Simon Fraser University)
报告时间：2025年11月28日（星期五）10:00-11:30
报告地点：腾讯会议：192-672-111
报告摘要：Ordinary Differential Equations (ODEs) are fundamental for modeling dynamic processes across numerous disciplines, including epidemiology, biology, physics, and economics. However, parameter estimation and inference in ODE-based models remain challenging due to inherent nonlinearities, high-dimensional parameter spaces, multimodal likelihood surfaces, and noisy observational data. In this talk, I will introduce our proposed sequential Monte Carlo (SMC) method as a powerful tool for performing Bayesian inference in ODE models. We demonstrate its effectiveness through simulation studies and real-world analyses of COVID-19 data from British Columbia.
报告人简介：王亮亮，加拿大Simon Fraser University统计与精算科学系副教授。于北京大学、McGill University获硕士学位，在University of British Columbia获统计学博士学位。其研究领域涵盖贝叶斯统计、序列蒙特卡洛方法、机器学习、生物统计学等，在微分方程建模、系统发育推断等方向成果丰硕，发表论文 60 余篇，部分成果发表于《Journal of the American Statistical Association》《Bioinformatics》等顶刊。
邀请人：潘灯
