By Ming-Hui Chen, Lynn Kuo, Paul O. Lewis
Offering a wealthy range of types, Bayesian phylogenetics permits evolutionary biologists, systematists, ecologists, and epidemiologists to procure solutions to very specified phylogenetic questions. appropriate for graduate-level researchers in records and biology, Bayesian Phylogenetics: tools, Algorithms, and Applications provides a photo of present traits in Bayesian phylogenetic examine.
Encouraging interdisciplinary learn, this ebook introduces state of the art phylogenetics to the Bayesian statistical neighborhood and, likewise, offers state of the art Bayesian facts to the phylogenetics neighborhood. The publication emphasizes version choice, reflecting fresh curiosity in adequately estimating marginal likelihoods. It additionally discusses new methods to enhance blending in Bayesian phylogenetic analyses within which the tree topology varies. moreover, the e-book covers divergence time estimation, biologically reasonable types, and the burgeoning interface among phylogenetics and inhabitants genetics.
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Additional info for Bayesian Phylogenetics: Methods, Algorithms, and Applications
Example text
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).
If the rate multiplier is close to 0, the site is close to being invariable. A large rate multiplier suggests that the site is a substitution hotspot. In Bayesian implementations, the gamma shape parameter α can be assigned a uniform prior U(0, A) with A = 200 in MrBayes, although an exponential prior may be preferable since most estimates of α from real datasets are small (< 1) (Yang, 1996). Instead of the parametric gamma distribution, several authors suggested using nonparametric Dirichlet process to model variable rates at sites (Lartillot and Philippe, 2004; Huelsenbeck and Suchard, 2007), with both the number of rate classes and the assignments of sites to classes treated as random variables (Ferguson, 1973a; Antoniak, 1974a).
2 Objective versus subjective priors . . . . . . . . . . . . Estimation of distance between two sequences . . . . . . . . . 1 The maximum likelihood estimate (MLE) . . . . . . . 2 Uniform or flat priors . . . . . . . . . . . . . . . . . . 3 The Jeffreys priors . . . . . . . . . . . . . . . . . . . . 4 Reference priors . . . . . . . . . . . . . . . . . . . . . Priors on model parameters in Bayesian phylogenetics .