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Accelerating Bayesian inference for evolutionary biology models

MOTIVATION: 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...

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Detalles Bibliográficos
Autores principales: Meyer, Xavier, Chopard, Bastien, Salamin, Nicolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408833/
https://www.ncbi.nlm.nih.gov/pubmed/28025203
http://dx.doi.org/10.1093/bioinformatics/btw712
Descripción
Sumario:MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/XavMeyer/hogan SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.