Bayesian phylogenetics : methods, algorithms, and applications / [edited by] Ming-Hui Chen, Lynn Kuo and Paul O. Lewis.

Includes bibliographical references and index.

1: Bayesian phylogenetics: methods, computational algorithms, and applications;
2: Priors in Bayesian phylogenetics;
3: Inated density ratio (IDR) method for estimating marginal likelihoods in Bayesian phylogenetics;
4: Bayesian model selection in phylogenetics and genealogy-based population genetics;
5: Variable tree topology stepping-stone marginal likelihood estimation;
6: Consistency of marginal likelihood estimation when topology varies.
7: Bayesian phylogeny analysis;
8: SMC (sequential Monte Carlo) for Bayesian phylogenetics;
9: Population model comparison using multi-locus datasets;
10: Bayesian methods in the presence of recombination;
11: Bayesian nonparametric phylodynamics;
12: Sampling and summary statistics of endpoint-conditioned paths in DNA sequence evolution;
13: Bayesian inference of species divergence times; Bibliography;
Index.

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-