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Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
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Improving Tree Probability Estimation with Stochastic Optimization and Variance Reduction.

Tianyu Xie1, Musu Yuan2, Minghua Deng3

  • 1School of Mathematical Sciences, Peking University, Beijing, 100871, China.

Arxiv
|September 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces efficient methods for training subsplit Bayesian networks (SBNs), improving phylogenetic tree topology probability estimation. Variance reduction techniques enhance performance for both SBN parameter learning and variational Bayesian phylogenetic inference.

Keywords:
probabilistic graphical modelsstochastic expectation maximizationtree probability estimationvariance reductionvariational Bayesian phylogenetic inference

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Machine Learning

Background:

  • Phylogenetic tree topology probability estimation is crucial for evolutionary studies.
  • Subsplit Bayesian networks (SBNs) offer a powerful probabilistic graphical model for this task.
  • Current expectation maximization (EM) methods for SBN parameter learning face scalability challenges with large datasets.

Purpose of the Study:

  • To develop computationally efficient methods for training SBNs.
  • To enhance SBN parameter optimization for variational Bayesian phylogenetic inference (VBPI).
  • To improve the accuracy and scalability of phylogenetic inference using SBNs.

Main Methods:

  • Introduction of novel, computationally efficient algorithms for SBN training.
  • Application of variance reduction techniques to optimize SBN parameters.
  • Integration of variance reduction into variational Bayesian phylogenetic inference (VBPI) for SBNs.

Main Results:

  • Developed methods demonstrate significant computational efficiency gains for SBN training.
  • Variance reduction techniques prove effective in improving SBN parameter optimization.
  • Proposed methods outperform baseline approaches in both tree topology probability estimation and VBPI.

Conclusions:

  • The new methods offer scalable and efficient solutions for phylogenetic inference using SBNs.
  • Variance reduction is a key factor for enhancing the performance of SBNs.
  • This work advances the field of computational phylogenetics by providing improved tools for analyzing large evolutionary datasets.