Bayesian Evolutionary Analysis with Beast


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Table of Contents

Preface; Acknowledgements; Part I. Theory: 1. Introduction; 2. Evolutionary trees; 3. Substitution and site models; 4. The molecular clock; 5. Structured trees and phylogeography; Part II. Practice: 6. Bayesian evolutionary analysis by sampling trees; 7. Setting up and running a phylogenetic analysis; 8. Estimating species trees from multilocus data; 9. Advanced analysis; 10. Posterior analysis and post-processing; 11. Exploring phylogenetic tree space; Part III. Programming: 12. Getting started with BEAST; 13. BEAST XML; 14. Coding and design patterns; 15. Putting it all together; Bibliography; List of authors; List of subjects.

Promotional Information

Covers theory, practice and programming in Bayesian phylogenetics with BEAST. The why, how and what of BEAST 2.

About the Author

Alexei J. Drummond is Professor of Computational Biology and Principal Investigator at the Allan Wilson Centre of Molecular Ecology and Evolution at the University of Auckland, New Zealand. He is the lead author of the BEAST software package and has gained a reputation in the field as one of the most knowledgeable experts for Bayesian evolutionary analyses. Remco R. Bouckaert is a computer scientist with a strong background in Bayesian methods. He is the main architect of version 2 of BEAST and has been working on extensions to the BEAST software and other phylogenetics projects in Alexei Drummond's group at the University of Auckland.


'Want to construct a phylogeny, add in calibrated time points or work out the past history of an epidemic? The open source package BEAST has established itself as the industry standard for all this and more. This definitive book, explaining what is under the hood, how the user can customize extensions and, most critically, a simple 'how to' users guide, is necessary reading for beginners and specialists alike.' Laurence D. Hurst, University of Bath
'In concert with the dramatic improvements to DNA sequencing technology, Bayesian inference has revolutionized population genetics, phylogenetics, and divergence time estimation. A similar impact on epidemiology appears imminent via a suite of new Bayesian methods that incorporate host and pathogen DNA sequence data into established mathematical frameworks. This book is an accessible and thorough introduction to these Bayesian procedures. However, the book does far more than explain the theory. It also includes clear guides on how to use the BEAST 2 software for performing Bayesian analyses, and how to visualize the results. Because the software is designed to be extensible, the book instructs users to write their own code to supplement the diverse methods that are already implemented in BEAST 2. This book is timely and is written by two of the leaders of the field. I highly recommend it.' Jeff Thorne, North Carolina State University

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