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An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics.

Liangliang Wang1, Shijia Wang1, Alexandre Bouchard-Côté2

  • 1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada.

Systematic Biology
|June 8, 2019
PubMed
Summary
This summary is machine-generated.

We introduce annealed Sequential Monte Carlo (SMC), an "embarrassingly parallel" Bayesian phylogenetic inference method. This approach offers an unbiased marginal likelihood estimator and integrates seamlessly with existing Markov chain Monte Carlo (MCMC) software.

Keywords:
Marginal likelihoodSequential Monte Carlophylogenetics

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Statistical Genetics

Background:

  • Bayesian phylogenetic inference is crucial for understanding evolutionary relationships.
  • Accurate estimation of marginal likelihood is essential for model selection in phylogenetics.
  • Existing methods often face computational challenges and limitations in scalability.

Purpose of the Study:

  • To develop a novel, efficient, and scalable method for Bayesian phylogenetic inference.
  • To provide an unbiased estimator for marginal likelihood, enabling robust software testing.
  • To facilitate the integration of advanced computational techniques into existing phylogenetic software.

Main Methods:

  • Annealed Sequential Monte Carlo (SMC) algorithm, an "embarrassingly parallel" approach.
  • Adaptive determination of annealing parameters for improved efficiency.
  • Integration with standard Markov chain Monte Carlo (MCMC) tree moves.

Main Results:

  • The annealed SMC method provides an approximate posterior distribution over trees and evolutionary parameters.
  • An unbiased estimator for the marginal likelihood was achieved.
  • Performance was evaluated against existing computational Bayesian phylogenetic and marginal likelihood estimation methods.

Conclusions:

  • The annealed SMC method is a computationally efficient and scalable approach for Bayesian phylogenetics.
  • Its unbiased marginal likelihood estimation aids in validating phylogenetic software.
  • The method's compatibility with MCMC moves simplifies its implementation in existing software packages.