Bayesian Phylogenetics: Methods, Algorithms, and by Ming-Hui Chen, Lynn Kuo, Paul O. Lewis PDF

By Ming-Hui Chen, Lynn Kuo, Paul O. Lewis

ISBN-10: 1466500794

ISBN-13: 9781466500792

Offering a wealthy variety of versions, Bayesian phylogenetics permits evolutionary biologists, systematists, ecologists, and epidemiologists to procure solutions to very distinct phylogenetic questions. appropriate for graduate-level researchers in information and biology, Bayesian Phylogenetics: equipment, Algorithms, and Applications provides a photograph of present traits in Bayesian phylogenetic study.

Encouraging interdisciplinary examine, this booklet introduces cutting-edge phylogenetics to the Bayesian statistical neighborhood and, likewise, provides state of the art Bayesian statistics to the phylogenetics group. The e-book emphasizes version choice, reflecting contemporary curiosity in competently estimating marginal likelihoods. It additionally discusses new methods to enhance blending in Bayesian phylogenetic analyses within which the tree topology varies. furthermore, the e-book covers divergence time estimation, biologically life like versions, and the burgeoning interface among phylogenetics and inhabitants genetics.

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Additional info for Bayesian Phylogenetics: Methods, Algorithms, and Applications

Sample text

3 The Jeffreys priors . . . . . . . . . . . . . . . . . . . . 4 Reference priors . . . . . . . . . . . . . . . . . . . . . Priors on model parameters in Bayesian phylogenetics . . . . . 1 Priors on branch lengths . . . . . . . . . . . . . . . . 2 Priors on parameters in substitution models . . . . . . 3 Priors for heterogeneous substitution rates among sites and over time . . . . . . . . . . . . . . . . . .

Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . 1 Bayesian Phylogenetics: Methods, Algorithms, and Applications Introduction The Bayesian paradigm The key feature of Bayesian statistics is its use of probability distributions to represent the uncertainty in the parameters of the model. The distribution of the parameters before the collection and analysis of the data is called the prior, while the distribution incorporating the information in the data is called the posterior.

Substitution models: a brief overview . . . . . . . . . . . . . . 1 Bayesian inference for substitution models . . . . . . . Bayesian model choice . . . . . . . . . . . . . . . . . . . . . . Computational tools for Bayesian model evidence . . . . . . . 1 Harmonic mean estimators . . . . . . . . . . . . . . . 2 IDR: inflated density ratio estimator . . . . . . . . . . 1 IDR: numerical examples . . . . . . . .

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Bayesian Phylogenetics: Methods, Algorithms, and Applications by Ming-Hui Chen, Lynn Kuo, Paul O. Lewis


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