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

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

Offering a wealthy variety of versions, Bayesian phylogenetics permits evolutionary biologists, systematists, ecologists, and epidemiologists to acquire solutions to very designated phylogenetic questions. appropriate for graduate-level researchers in facts and biology, Bayesian Phylogenetics: equipment, Algorithms, and Applications provides a picture of present traits in Bayesian phylogenetic learn.

Encouraging interdisciplinary examine, this booklet introduces cutting-edge phylogenetics to the Bayesian statistical group and, likewise, provides state of the art Bayesian statistics to the phylogenetics group. The booklet emphasizes version choice, reflecting fresh curiosity in properly estimating marginal likelihoods. It additionally discusses new ways to enhance blending in Bayesian phylogenetic analyses during which the tree topology varies. additionally, the publication covers divergence time estimation, biologically practical 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

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction 1 2 Bayesian statistics experienced a Renaissance with the advent of Markov chain Monte Carlo simulation, which made possible the analysis of posterior distributions that are analytically intractable (Geman and Geman, 1984; Gelfand and Smith, 1990). Phylogenetics transitioned to a likelihood-based framework with Felsenstein’s seminal paper (Felsenstein, 1981), and Bayesian approaches to phylogenetics were introduced in the mid-1990s with papers by Rannala and Yang (1996) and Mau and Newton (1997).

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.

Priors on the tree topology . . . . . . . . . . . . . . . . . . . 1 Prior on rooted trees . . . . . . . . . . . . . . . . . . 2 probabilities . . . . . . . . . . . . . . . . . . . . . . . 3 Prior on trees and high posterior probabilities . . . . . Priors on times and rates for estimation of divergence times . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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