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Deflating trees: improving Bayesian branch-length estimates using informed priors.

Bradley J Nelson1, John J Andersen1, Jeremy M Brown2

  • 1Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.

Systematic Biology
|January 18, 2015
PubMed
Summary
This summary is machine-generated.

Informed priors improve phylogenetic analyses by incorporating outside information, leading to better branch-length estimates compared to default settings. This approach enhances Bayesian inference accuracy in phylogenetics.

Keywords:
Bayesian phylogeneticsbranch lengthsprior choice

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Statistical modeling

Background:

  • Bayesian analyses are sensitive to prior distributions, especially in phylogenetics where branch-length priors significantly impact results.
  • Current methods lack a consensus on optimal prior setting strategies, particularly for branch lengths.

Purpose of the Study:

  • To investigate the utility of informed branch-length priors in phylogenetic analyses.
  • To compare phylogenetic inferences using informed priors versus default prior settings.

Main Methods:

  • Explored the use of external information to define informed priors for branch lengths.
  • Compared results from analyses using informed priors with those using default exponential and compound Dirichlet priors.
  • Evaluated inferences for datasets yielding problematic branch- and tree-length estimates under default settings.

Main Results:

  • Informed priors, utilizing relevant outside information, improved phylogenetic inferences for both exponential and compound Dirichlet distributions.
  • The benefits were most pronounced for datasets that exhibited issues with branch- and tree-length estimation using default priors.

Conclusions:

  • Incorporating outside information to set informed branch-length priors enhances the accuracy of phylogenetic analyses.
  • Informed priors offer a promising avenue for improving Bayesian inference in phylogenetics, particularly for challenging datasets.