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Reliability of Bayesian Posterior Probabilities and Bootstrap Frequencies in Phylogenetics
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Evolutionary Biology, Systematic Botany.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Evolutionary Biology, Systematic Botany.
2003 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 52, no 5, 665-673 p.Article in journal (Refereed) Published
Abstract [en]

Many empirical studies have revealed considerable differences between nonparametric bootstrapping and Bayesian posterior probabilities in terms of the support values for branches, despite claimed predictions about their approximate equivalence. We investigated this problem by simulating data, which were then analyzed by maximum likelihood bootstrapping and Bayesian phylogenetic analysis using identical models and reoptimization of parameter values. We show that Bayesian posterior probabilities are significantly higher than corresponding nonparametric bootstrap frequencies for true clades, but also that erroneous conclusions will be made more often. These errors are strongly accentuated when the models used for analyses are underparameterized. When data are analyzed under the correct model, nonparametric bootstrapping is conservative. Bayesian posterior probabilities are also conservative in this respect, but less so.

Place, publisher, year, edition, pages
2003. Vol. 52, no 5, 665-673 p.
National Category
Probability Theory and Statistics Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-96721DOI: 10.1080/10635150390235485OAI: oai:DiVA.org:uu-96721DiVA: diva2:171391
Available from: 2008-02-13 Created: 2008-02-13 Last updated: 2012-07-26Bibliographically approved
In thesis
1. On Estimating Topology and Divergence Times in Phylogenetics
Open this publication in new window or tab >>On Estimating Topology and Divergence Times in Phylogenetics
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This PhD thesis consists of an introduction and five papers, dealing with statistical methods in phylogenetics.

A phylogenetic tree describes the evolutionary relationships among species assuming that they share a common ancestor and that evolution takes place in a tree like manner. Our aim is to reconstruct the evolutionary relationships from aligned DNA sequences.

In the first two papers we investigate two measures of confidence for likelihood based methods, bootstrap frequencies with Maximum Likelihood (ML) and Bayesian posterior probabilities. We show that an earlier claimed approximate equivalence between them holds under certain conditions, but not in the current implementations of the two methods.

In the following two papers the divergence times of the internal nodes are considered. The ML estimate of the divergence time of the root is improved if longer sequences are analyzed or if more taxa are added. We show that the gain in precision is faster with longer sequences than with more taxa. We also show that the algorithm of the software package PATHd8 may give biased estimates if the global molecular clock is violated. A change of the algorithm to obtain unbiased estimates is therefore suggested.

The last paper deals with non-informative priors when using the Bayesian approach in phylogenetics. The term is not uniquely defined in the literature. We adopt the idea of data translated likelihoods and derive the so called Jeffreys' prior for branch lengths using Jukes Cantor model of evolution.

Place, publisher, year, edition, pages
Uppsala: Avdelningen för matematisk statistik, 2008. 53 p.
Series
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 55
Keyword
Mathematical statistics, Phylogenetics, Divergence Time, Likelihood based methods, Non-informative prior, bootstrap support, Matematisk statistik
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-8441 (URN)978-91-506-1988-1 (ISBN)
Public defence
2008-03-07, Siegbahnsalen, Ångström Laboratory, Lägerhyddsvägen 1, Uppsala, 13:15
Opponent
Supervisors
Available from: 2008-02-13 Created: 2008-02-13 Last updated: 2012-07-26Bibliographically approved

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