Sequential Monte Carlo methods with non-standard applications
数学专题报告
报告题目(Title)：Sequential Monte Carlo methods with non-standard applications
报告人(Speaker)：王亮亮（Simon Fraser University）
地点(Place)：Zoom会议（会议号85839679693，密码406630）
时间(Time)：2026年4月29日（周三）10:00-11:00
邀请人(Inviter)：熊云丰
报告摘要
Modern Bayesian inference frequently requires sampling from complex, high-dimensional posterior distributions. While standard Markov chain Monte Carlo (MCMC) algorithms are foundational, they often exhibit poor mixing, struggle with local transition efficiency, and become trapped in local optima when applied to highly structured parameter spaces, such as discrete combinatorial spaces or multimodal posterior landscapes. In this talk, I will introduce Sequential Monte Carlo (SMC) methods—specifically Annealed Sequential Monte Carlo (ASMC)—as a powerful and "embarrassingly parallel" alternative for Bayesian computation. ASMC efficiently provides an approximate posterior distribution alongside an unbiased estimator for the marginal likelihood, which is highly valuable for conducting Bayesian model comparison and testing the correctness of posterior simulations. I will demonstrate the adaptability, scalability, and efficiency of SMC through two primary non-standard applications involving complex statistical models. First, I will discuss Bayesian phylogenetic inference, where we utilize SMC to navigate the highly discrete and computationally challenging space of evolutionary trees using biological sequence data. Second, I will present the application of SMC for parameter estimation and model selection in nonlinear ordinary differential equations (ODEs), with a specific focus on infectious disease transmission models.
主讲人简介
Liangliang Wang is an Associate Professor in the Department of Statistics and Actuarial Science at Simon Fraser University, where she has been a faculty member since 2013. Dr. Wang earned her Ph.D. in Statistics from the University of British Columbia, following graduate studies at McGill University and Peking University, and a B.Sc. in Computer Science from Zhengzhou University. Her research advances computational statistics and statistical machine learning, with a specific focus on scalable Bayesian inference, Monte Carlo methods, and complex probabilistic modeling. She actively develops methodologies to address critical, large-scale data challenges in genetics, public health, biology, and environmetrics. Supported by major funding from NSERC, CANSSI, and Genome BC, Dr. Wang has published about 60 peer-reviewed papers in top-tier statistical journals and machine learning conferences. Website: https://www.sfu.ca/~lwa68/
