Accelerating Bayesian inference for evolutionary biology models.

Détails

Ressource 1Télécharger: 28025203_BIB_512001709D42.pdf (453.73 [Ko])
Etat: Serval
Version: Final published version
ID Serval
serval:BIB_512001709D42
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Accelerating Bayesian inference for evolutionary biology models.
Périodique
Bioinformatics
Auteur(s)
Meyer X., Chopard B., Salamin N.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Statut éditorial
Publié
Date de publication
2017
Peer-reviewed
Oui
Volume
33
Numéro
5
Pages
669-676
Langue
anglais
Résumé
Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods.
We present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models. We show on a parameter-rich macroevolutionary model increases of the sampling speed up to 35 times with 32 processors when compared to a sequential M-H process. More importantly, our framework achieves up to a twentyfold faster convergence to estimate the posterior probability of phylogenetic trees using 32 processors when compared to the well-known software MrBayes for Bayesian inference of phylogenetic trees.
https://bitbucket.org/XavMeyer/hogan.
nicolas.salamin@unil.ch.
Supplementary data are available at Bioinformatics online.

Pubmed
Web of science
Création de la notice
03/01/2017 19:23
Dernière modification de la notice
03/03/2018 17:10
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