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.
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