时 间：2022年11月25日（周五） 14：00-15：00
地 点：理科大楼 A1314；腾讯会议 844-333-509
主 题：Bayesian phylogenetic inference via sequential Monte Carlo approaches
主讲人：汪时嘉 南开大学副教授
主持人：王小舟 助理教授
主 办：统计学院
摘 要：
Phylogenetic tree reconstruction is a main task in evolutionary biology. Traditional MCMC methods may suffer from the curse of dimensionality and the local-trap problem. Sequential Monte Carlo methods have emerged as alternatives to MCMC methods for phylogenetic reconstruction. Firstly, we introduce a new combinatorial SMC method, with a novel and efficient proposal distribution. We also explore combining SMC and Gibbs sampling to jointly estimate the phylogenetic trees and evolutionary parameter of genetic datasets. Secondly, we propose an “embarrassingly parallel” method for Bayesian phylogenetic inference, annealed SMC, based on recent advances in the SMC literature such as adaptive determination of annealing parameters.
报告人简介：
Shijia Wang is an associate professor in School of Statistics and Data Science, Nankai University, where he has been a faculty member since 2019. Before that, he received his PhD in statistics at Simon Fraser University, Canada. His research interest involves computational statistics, statistical machine learning and computational biology.