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NIMBioS Accelerator Tutorial

RevBayes: Bayesian Inference of Phylogeny

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Topic: RevBayes: A program for Bayesian inference of phylogeny using probabilistic graphical models and an interpreted language

Dates: August 7-11, 2017

Location: NIMBioS



This course featured RevBayes, an exciting new program for Bayesian inference of phylogeny. RevBayes is the successor to the popular program MrBayes, but represents both a complete rewrite of the computer code and a fundamental re-conception of phylogenetic models. Specifically, RevBayes adopts a 'graphical-model' framework that views all statistical models as comprised of components that can be assembled in myriad configurations to explore a corresponding array of questions. This graphical-model approach to phylogenetic inference provides effectively infinite flexibility. Moreover, the graphical models are specified using an R-like language, Rev, that empowers users to construct arbitrarily complex phylogenetic models from simple component parts (i.e. random variables, parameter transformations and constants of different sorts).

This course was focused on phylogenetic trees and comparative-phylogenetic methods, including divergence-time estimation, morphological evolution, lineage diversification, and historical biogeography. Instruction was based on a combination of carefully tailored lectures introducing the theoretical and conceptual basis of each inference problem and hands-on computer tutorials demonstrating how to explore these questions using RevBayes (see

Participants were not assumed to have expertise in phylogenetic theory; rather, we provided an accessible introduction to Bayesian statistical inference and stochastic models. We assumed only that participants were familiar with phylogenetic trees and their applications to evolutionary biology. We therefore anticipated that this course would be most suitable for senior PhD students, postdoctoral researchers, and faculty who wanted to learn these techniques.

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From 2008 until early 2021, NIMBioS was supported by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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