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Bayesian phylogenetics : methods, algorithms, and applications

Author: Ming-Hui Chen; Lynn Kuo; Paul O Lewis
Publisher: Boca Raton, FL : CRC Press/Taylor and Francis Group, [2014] ©2014
Series: Chapman and Hall/CRC mathematical & computational biology series.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, Algorithms, and Applications presents a snapshot of current trends in Bayesian phylogenetic research. Encouraging  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Erscheint auch als:
Chen, Ming-Hui. Bayesian Phylogenetics .
Methods, Algorithms, and Applications
Druck-Ausgabe
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ming-Hui Chen; Lynn Kuo; Paul O Lewis
ISBN: 9781466500822 1466500824
OCLC Number: 880825218
Description: 1 online resource (xxxi, 391 pages) : illustrations.
Contents: Front Cover; Contents; List of Figures; List of Tables; Preface; Editors; Contributors; Chapter 1: Bayesian phylogenetics: methods, computational algorithms, and applications; Chapter 2: Priors in Bayesian phylogenetics; Chapter 3: Inated density ratio (IDR) method for estimating marginal likelihoods in Bayesian phylogenetics; Chapter 4: Bayesian model selection in phylogenetics and genealogy-based population genetics; Chapter 5: Variable tree topology stepping-stone marginal likelihood estimation; Chapter 6: Consistency of marginal likelihood estimation when topology varies. Chapter 7: Bayesian phylogeny analysisChapter 8: SMC (sequential Monte Carlo) for Bayesian phylogenetics; Chapter 9: Population model comparison using multi-locus datasets; Chapter 10: Bayesian methods in the presence of recombination; Chapter 11: Bayesian nonparametric phylodynamics; Chapter 12: Sampling and summary statistics of endpoint-conditioned paths in DNA sequence evolution; Chapter 13: Bayesian inference of species divergence times; Bibliography; Back Cover.
Series Title: Chapman and Hall/CRC mathematical & computational biology series.
Responsibility: edited by Ming-Hui Chen, Lynn Kuo, and Paul O. Lewis.

Abstract:

Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, Algorithms, and Applications presents a snapshot of current trends in Bayesian phylogenetic research. Encouraging interdisciplinary research, this book introduces state-of-the-art phylogenetics to the Bayesian statistical community and, likewise, presents state-of-the-

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Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Read more...

 
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