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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Improving marginal likelihood estimation for Bayesian phylogenetic model selection.

Wangang Xie1, Paul O Lewis, Yu Fan

  • 1Abbott, 100 Abbott Park, R436/AP9A-2, Abbott Park, IL 60064, USA.

Systematic Biology
|December 29, 2010
PubMed
Summary
This summary is machine-generated.

The harmonic mean method often overestimates marginal likelihoods in phylogenetics. Steppingstone sampling offers improved accuracy over harmonic mean and thermodynamic integration for model comparison.

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Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Bayesian statistics

Background:

  • Marginal likelihood is crucial for Bayesian phylogenetic model comparison and Bayes Factors.
  • The harmonic mean (HM) method is a popular but often inaccurate estimator of marginal likelihood.
  • Thermodynamic integration (TI) is more accurate than HM but computationally intensive.

Purpose of the Study:

  • Introduce and evaluate a new method, steppingstone sampling (SS), for estimating marginal likelihoods.
  • Compare the accuracy and performance of SS against HM and TI methods.
  • Provide recommendations for accurate phylogenetic model selection.

Main Methods:

  • Developed steppingstone sampling (SS) using importance sampling across a series of distributions.
  • Applied SS, TI, and HM methods to simulated and real phylogenetic datasets.
  • Evaluated estimation accuracy and computational efficiency of each method.

Main Results:

  • Steppingstone sampling (SS) and thermodynamic integration (TI) demonstrated significantly higher accuracy than the harmonic mean (HM) method.
  • Both SS and TI provided more reliable estimates of marginal likelihoods.
  • The improved accuracy of SS and TI justifies their use despite increased computational demands.

Conclusions:

  • The harmonic mean (HM) method's tendency to overestimate marginal likelihoods makes it unreliable for phylogenetic model comparison.
  • Steppingstone sampling (SS) and thermodynamic integration (TI) are recommended as more accurate alternatives.
  • Accurate marginal likelihood estimation is essential for robust Bayesian phylogenetic inference and model selection.