Abstract Detail

Zenil-Ferguson, Rosana [1], Freyman, Will [2], Jordan, Koch [3].

RevBayes Tutorial: An introduction to Bayesian inference in Phylogenetics.

The study of plant systematics and phenotypic evolution has provided unparalleled statistical challenges when building phylogenetic trees and understanding trait evolution. In this workshop, we will introduce RevBayes, a powerful computational program that allows users to perform a large number of complex statistical inferences in a phylogenetic context. RevBayes is a graphical-model based software that allows scientists to specify models in an accessible manner while encouraging the construction of complex phylogenetic models by using basic mathematical definitions. During the workshop, we will provide a lecture introducing the theoretical background necessary to understand the models and bayesian inference jointly with a hands-on computer tutorial demonstrating how to explore phylogenetic inferences using RevBayes ( http://revbayes.github.io/tutorials.html). Furthermore, we will show how to use RevBayes to build phylogenies and some comparative phylogenetic methods (i.e. discrete trait models, diversification). Participants are not assumed to have expertise in phylogenetic theory. However, we expect participants to be familiar with phylogenetic trees and their applications to evolutionary biology. We anticipate this workshop to be mostly suitable for PhD candidates, postdoctoral researchers, and faculty who want to learn these techniques. Participants need to bring their own laptop to connect to wired/wireless internet.

Related Links:
RevBayes Tutorials

1 - University of Minnesota, Department of Ecology, Evolution & Behavio, 1479 Gortner Ave, Suite 140 , St Paul, MN, 55108, USA
2 - University of Minnesota, Department of Ecology, Evolution and Behaviour, 1479 Gortner Ave, Suite 140 , St Paul, MN, 55108, USA
3 - University of Minnesota, Department of Ecology, Evolution, and Behavior, 1479 Gortner Ave, Suite 140 , St Paul, MN, 55108, USA

Phylogenetic Comparative Methods
Bayesian Statistics.

Presentation Type: Workshop
Abstract ID:44
Candidate for Awards:None

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