Online Program Home
My Program

Abstract Details

Activity Number: 514 - Advanced Statistical Inference for Stochastic Models of Evolutionary Biology
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #329657 Presentation
Title: Bayesian Nonparametrics for Evolutionary Model Selection
Author(s): Mandev Gill*
Companies:
Keywords: Phylogenetics; Bayesian Inference; Nonparameterics; Markov Chain Monte Carlo; Computational Statistics; Genetics
Abstract:

Specification of appropriate evolutionary models is a crucial step in phylogenetic inference. Evolutionary model selection has traditionally been accomplished by first partitioning a multiple sequence alignment and then estimating the best-fitting evolutionary model for each partition. However, this can be a difficult and time-consuming approach with many limitations. Automatic model selection approaches can address such issues as well as yield better fitting models by simultaneously estimating the number of partitions, assignment of sites to partitions, and the substitution model for each partition. Recent progress has been made in this direction through the development of a Dirichlet process model, and we take this development a step further by employing a generalized Polya urn process. A generalized Polya urn process includes a large number of countable mixture models as special cases, and we examine the effectiveness of different mixture models in improving evolutionary model fit. We also develop Markov chain Monte Carlo (MCMC) algorithms that exploit data squashing techniques to reduce numerical booking and can efficiently explore the large phylogenetic model space.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program