Erick Matsen

Erick Matsen

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Additional Details

Affiliation - Fred Hutchinson Cancer Research Center

Title of Talk - Making Bayesian phylogenetics like training a neural network

Abstract

Bayesian posterior distributions on phylogenetic trees remain difficult to sample despite decades of effort. The complex discrete and continuous model structure of trees means that recent inference methods developed for Euclidean space are not easily applicable to the phylogenetic case. Thus, we are left with random-walk Markov Chain Monte Carlo (MCMC) with uninformed tree modification proposals; these traverse tree space slowly because phylogenetic posteriors are concentrated on a small fraction of the very many possible trees. In this talk, I will describe our wild adventure developing efficient alternatives to random-walk MCMC, which has concluded successfully with the development of a variational Bayes formulation of Bayesian phylogenetics. This formulation leverages a “factorization

 

Date And Time

Feb 14, 2020 04:00 PM to
Dec 9, 2022 05:00 PM
 

Location

Online Event
 

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