Bayesian Inference for Phylogenetic Trees and Networks

• Activity Number: 8 - Computational Methods and Bayesian Inference for Networks
• Type: Invited
• Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Council of Chapters
• Author(s): Liangliang Wang* and Shijia Wang and Alexandre Bouchard-Côté
• Companies: Simon Fraser University and Simon Fraser University and University of British Columbia
• Keywords: Sequential Monte Carlo; Markov chain Monte Carlo; Bayesian phylogenetics; phylogenetic trees; phylogenetic networks; hybridization

Abstract

Phylogenetic trees, playing a central role in biology, model evolutionary histories of taxa that range from genes to genomes, and to species. The goal of Bayesian phylogenetics is to approximate a posterior distribution of phylogenetic trees based on biological data. Standard Bayesian estimation of phylogenetic trees can handle rich evolutionary models but requires expensive Markov chain Monte Carlo (MCMC) simulations, which may suffer from the curse of dimensionality and the local-trap problem. We propose sequential Monte Carlo (SMC) methods as alternatives to MCMC in posterior inference over phylogenetic trees. Furthermore, the proposed SMC methods for trees are adapted for phylogenetic networks when evolutionary events such as hybridization are taken into consideration.