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 range of versions, Bayesian phylogenetics permits evolutionary biologists, systematists, ecologists, and epidemiologists to acquire solutions to very precise phylogenetic questions. appropriate for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: tools, Algorithms, and Applications provides a picture of present tendencies in Bayesian phylogenetic examine.

Encouraging interdisciplinary learn, this booklet introduces state of the art phylogenetics to the Bayesian statistical group and, likewise, offers state of the art Bayesian facts to the phylogenetics neighborhood. The ebook emphasizes version choice, reflecting fresh curiosity in thoroughly estimating marginal likelihoods. It additionally discusses new techniques to enhance blending in Bayesian phylogenetic analyses within which the tree topology varies. additionally, the booklet covers divergence time estimation, biologically reasonable types, and the burgeoning interface among phylogenetics and inhabitants genetics.

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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. In contrast, in Frequentist statistics, a parameter is an unknown but fixed constant and cannot have a distribution.

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. In contrast, in Frequentist statistics, a parameter is an unknown but fixed constant and cannot have a distribution.

They point out the difficulties of these algorithms with highdimensional summations and integrals and local trapping. So they propose a sequential stochastic approximation Monte Carlo (SSAMC) algorithm which is based on SAMC by Liang et al. (2007) and Cheon and Liang (2008). They show that SSAMC can avoid the local trapping problem because of its self-adjusting mechanism. They further show that SSAMC can be computationally efficient by comparing its CPU time per iteration to that of BAMBE (Simon and Larget, 2001) and MRBAYES (Huelsenbeck and Ronquist, 2001).

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