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LoRaD: Marginal likelihood estimation with haste (but no waste).

Yu-Bo Wang1, Analisa Milkey2, Aolan Li3

  • 1School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USA.

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The new Lowest Radial Distance (LoRaD) method accurately estimates model marginal likelihood in Bayesian phylogenetics. It simplifies calculations by only requiring posterior distribution sampling, outperforming existing methods.

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Area of Science:

  • Computational Biology
  • Statistical Modeling
  • Evolutionary Biology

Background:

  • Bayesian model selection relies on accurate marginal likelihood estimation.
  • Current methods like Steppingstone/Thermodynamic Integration require complex power posterior sampling.
  • Faster methods like Generalized Harmonic Mean (GHM) may lack accuracy.

Purpose of the Study:

  • Introduce the Lowest Radial Distance (LoRaD) method for marginal likelihood estimation.
  • Compare LoRaD's performance against existing Bayesian phylogenetics methods.
  • Assess LoRaD's applicability in both fixed and variable tree topology analyses.

Main Methods:

  • LoRaD modifies the Partition-Weighted Kernel method.
  • It requires sampling only from the posterior distribution.
  • The method was tested on a fixed-topology molecular phylogenetics example with 180 parameters.

Main Results:

  • LoRaD demonstrates improved accuracy compared to Generalized Harmonic Mean (GHM).
  • Performance is comparable to the Generalized Steppingstone method in fixed-topology analyses.
  • Accurate marginal likelihood estimation is possible in variable-topology cases under specific conditions.

Conclusions:

  • LoRaD offers a more efficient and accurate approach to marginal likelihood estimation in Bayesian phylogenetics.
  • The method simplifies computational requirements by avoiding power posterior sampling.
  • LoRaD shows promise for both fixed and variable tree topology analyses in evolutionary studies.