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 c

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Yes, you can access Bayesian Phylogenetics by Ming-Hui Chen, Lynn Kuo, Paul O. Lewis, Ming-Hui Chen,Lynn Kuo,Paul O. Lewis in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

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Publisher

Year

Print ISBN

eBook ISBN

Edition

Topic

Mathematics

Subtopic

Probability & Statistics

Index

Mathematics

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 analysis
Chapter 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

About this book

Frequently asked questions

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Table of contents