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Variational Bayesian phylogenies through matrix representation of tree space.

Remco R Bouckaert1

  • 1School of Computer Science, University of Auckland, Auckland, New Zealand.

Peerj
|May 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel matrix representation for phylogenetic tree distributions, enabling accurate modeling and parameter recovery. This approach improves upon summary trees and advances Bayesian phylogenetic inference scalability.

Keywords:
BEAST 2Bayesian phylogeneticsSummary treesTree spaceVariational Bayes

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Statistical Modeling

Background:

  • Phylogenetic trees are crucial for evolutionary studies.
  • Representing distributions of trees is computationally challenging.
  • Current methods often rely on single summary trees, losing information.

Purpose of the Study:

  • To develop a new method for representing distributions of rooted time trees.
  • To introduce an algorithm for efficient parameter recovery from these distributions.
  • To improve upon existing methods for phylogenetic inference and tree summarization.

Main Methods:

  • Utilizing distance matrices with multivariate normal distributions to model tree distributions.
  • Defining a specific subspace called a "cube" for bijective representation.
  • Developing and applying the "cubeVB" algorithm for parameter estimation.

Main Results:

  • Demonstrated accurate representation of published tree distributions.
  • Showed successful recovery of key phylogenetic parameters like tree height and length using "cubeVB".
  • Obtained improved summary trees compared to maximum clade credibility trees.

Conclusions:

  • Matrix representation offers a scalable approach to Bayesian phylogenetic inference.
  • The "cube" subspace provides a practical trade-off for bijective representation.
  • The "cubeVB" algorithm and package offer a valuable tool for phylogenetic research.