| 主讲人简介： | 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. He has published papers in statistical journals and machine learning conferences, including Journal of Computational and Graphical Statistics, Systematic Biology, Advances in Neural Information Processing Systems and Bioinformatics. |
| 讲座简介： | 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. |
