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Record Nr. |
UNINA9910787845903321 |
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Titolo |
Bayesian phylogenetics : methods, algorithms, and applications / / edited by Ming-Hui Chen, Lynn Kuo, and Paul O. Lewis, University of Connecticut Storrs, USA |
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Pubbl/distr/stampa |
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Boca Raton : , : CRC Press, , [2014] |
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©2014 |
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ISBN |
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0-429-18426-3 |
1-4665-0079-4 |
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Descrizione fisica |
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1 online resource (391 p.) |
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Collana |
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Chapman and Hall/CRC Mathematical and Computational Biology Series |
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Disciplina |
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Soggetti |
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Phylogeny |
Biometry |
Molecular genetics |
Bayesian statistical decision theory |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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 |
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Sommario/riassunto |
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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. 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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