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A Practical Guide to Phylogenetics for Nonexperts
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Choosing among partition models in Bayesian phylogenetics.

Yu Fan1, Rui Wu, Ming-Hui Chen

  • 1Department of Ecology and Evolutionary Biology, University of Connecticut.

Molecular Biology and Evolution
|August 31, 2010
PubMed
Summary
This summary is machine-generated.

A new generalized stepping-stone (SS) method accurately estimates marginal likelihoods for Bayesian phylogenetic analyses, improving upon the inaccurate harmonic mean (HM) method and leading to more reliable Bayes factor (BF) model selection.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Statistical Modeling

Background:

  • Bayesian phylogenetic analyses utilize Bayes factors (BFs) for model selection, often relying on marginal likelihood estimation.
  • The harmonic mean (HM) method is commonly used for marginal likelihood estimation but is known for its inaccuracy.
  • Previous methods like the stepping-stone (SS) approach have limitations in sampling from distributions close to the prior.

Purpose of the Study:

  • To introduce a novel, more accurate method for estimating marginal likelihoods in Bayesian phylogenetics.
  • To compare the performance of the new method against the harmonic mean (HM) and the original stepping-stone (SS) methods.
  • To evaluate the impact of different marginal likelihood estimation methods on partition model selection.

Main Methods:

  • Developed a generalized stepping-stone (SS) method using a reference distribution from posterior samples.
  • Compared the generalized SS method with the HM and original SS methods using simulated and empirical phylogenetic data.
  • Assessed model selection performance, focusing on partition model choices and Bayes factor (BF) values.

Main Results:

  • The generalized SS method provides more precise and reasonable Bayes factor (BF) values compared to the HM method.
  • The HM method tends to favor overpartitioned models, while the generalized SS method selects simpler, more biologically plausible partition schemes.
  • The generalized SS method demonstrates greater repeatability in BF values for the same data and partition model.

Conclusions:

  • The generalized SS method offers a significant improvement for accurate marginal likelihood estimation in Bayesian phylogenetics.
  • This new method addresses limitations of previous approaches, particularly regarding prior-dependent sampling.
  • Accurate marginal likelihood estimation is crucial for reliable model selection, guiding inferences in molecular evolution studies.