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Article
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Biometrics
Article . 1996 . Peer-reviewed
Data sources: Crossref
Biometrics
Article . 1997
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Bayesian Hypothesis Testing of Four-Taxon Topologies Using Molecular Sequence Data

Bayesian hypothesis testing of four-taxon topologies using molecular sequence data
Authors: Sinsheimer, Janet S.; Lake, James A.; Little, Roderick J. A.;

Bayesian Hypothesis Testing of Four-Taxon Topologies Using Molecular Sequence Data

Abstract

The reconstruction of phylogenetic trees from molecular sequences presents unusual problems for statistical inference. For example, three possible alternatives must be considered for four taxa when inferring the correct unrooted tree (referred to as a topology). In our view, classical hypothesis testing is poorly suited to this triangular set of alternative hypotheses. In this article, we develop Bayesian inference to determine the posterior probability that a four-taxon topology is correct given the sequence data and the evolutionary parsimony algorithm for phylogenetic reconstruction. We assess the frequency properties of our models in a large simulation study. Bayesian inference under the principles of evolutionary parsimony is shown to be well calibrated with reasonable discriminating power for a wide range of realistic conditions, including conditions that violate the assumptions of evolutionary parsimony.

Related Organizations
Keywords

Biometry, Base Sequence, Models, Genetic, molecular evolution, Bayesian inference, Molecular Sequence Data, Bayes Theorem, DNA, Protein sequences, DNA sequences, Applications of statistics to biology and medical sciences; meta analysis, phylogenetic trees, Evolution, Molecular, Problems related to evolution, Multivariate Analysis, Phylogeny

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
36
Top 10%
Top 10%
Top 10%
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