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RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language
Stockholm University, Faculty of Science, Department of Mathematics. University of California, USA.
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Number of Authors: 8
2016 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 65, no 4, 726-736 p.Article in journal (Refereed) Published
Abstract [en]

Programs for Bayesian inference of phylogeny currently implement a unique and inot signxed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be speciinot signed interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-speciinot signcation language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous inot sign,exibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our inot signeld. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at ext-link-type=uri xlink:href=> [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.].

Place, publisher, year, edition, pages
2016. Vol. 65, no 4, 726-736 p.
National Category
Biological Sciences
URN: urn:nbn:se:su:diva-134166DOI: 10.1093/sysbio/syw021ISI: 000381277700012PubMedID: 27235697OAI: diva2:1040788
Available from: 2016-10-28 Created: 2016-10-03 Last updated: 2016-10-28Bibliographically approved

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