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Full Description
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-art Bayesian statistics to the phylogenetics community. The book emphasizes model selection, reflecting recent interest in accurately estimating marginal likelihoods. It also discusses new approaches to improve mixing in Bayesian phylogenetic analyses in which the tree topology varies. In addition, the book covers divergence time estimation, biologically realistic models, and the burgeoning interface between phylogenetics and population genetics.
Contents
Bayesian phylogenetics: methods, computational algorithms, and applications. Priors in Bayesian phylogenetics. IDR for marginal likelihood in Bayesian phylogenetics. Bayesian model selection in phylogenetics and genealogy-based population genetics. Variable tree topology stepping-stone marginal likelihood estimation. Consistency of marginal likelihood estimation when topology varies. Bayesian phylogeny analysis. Sequential Monte Carlo (SMC) for Bayesian phylogenetics. Population model comparison using multi-locus datasets. Bayesian methods in the presence of recombination. Bayesian nonparametric phylodynamics. Sampling and summary statistics of endpoint-conditioned paths in DNA sequence evolution. Bayesian inference of species divergence times. Index.
