Phylogenetic Inference via Sequential Monte Carlo

DSpace/Manakin Repository

Phylogenetic Inference via Sequential Monte Carlo

Citable link to this page

 

 
Title: Phylogenetic Inference via Sequential Monte Carlo
Author: Bouchard-Côté, Alexandre; Jordan, Michael I.; Sankararaman, Sriram

Note: Order does not necessarily reflect citation order of authors.

Citation: Bouchard-Côté, Alexandre, Sriram Sankararaman, and Michael I. Jordan. 2012. Phylogenetic inference via sequential monte carlo. Systematic Biology 61(4): 579-593.
Full Text & Related Files:
Abstract: Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to Bayesian inference based on Markov chain Monte Carlo (MCMC) have not, however, kept pace with the scale of the data analysis problems in phylogenetics, and this has hindered the adoption of Bayesian methods. In this paper, we present an alternative to MCMC based on Sequential Monte Carlo (SMC). We develop an extension of classical SMC based on partially ordered sets and show how to apply this framework—which we refer to as PosetSMC—to phylogenetic analysis. We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data. The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence. We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC–SMC schemes. Software for PosetSMC is available at http://www.stat.ubc.ca/ bouchard/PosetSMC.
Published Version: doi:10.1093/sysbio/syr131
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3376373/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10436311
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters