Improving Tree Probability Estimation with Stochastic Optimization and Variance Reduction

TL;DR

This paper introduces efficient training methods for subsplit Bayesian networks in phylogenetics, utilizing variance reduction techniques to improve tree probability estimation and Bayesian inference on large datasets.

Contribution

It presents novel variance reduction methods for training SBNs, enhancing scalability and performance in phylogenetic tree probability estimation.

Findings

Outperforms baseline methods on synthetic data

Improves Bayesian phylogenetic inference accuracy

Enhances scalability for large datasets

Abstract

Probability estimation of tree topologies is one of the fundamental tasks in phylogenetic inference. The recently proposed subsplit Bayesian networks (SBNs) provide a powerful probabilistic graphical model for tree topology probability estimation by properly leveraging the hierarchical structure of phylogenetic trees. However, the expectation maximization (EM) method currently used for learning SBN parameters does not scale up to large data sets. In this paper, we introduce several computationally efficient methods for training SBNs and show that variance reduction could be the key for better performance. Furthermore, we also introduce the variance reduction technique to improve the optimization of SBN parameters for variational Bayesian phylogenetic inference (VBPI). Extensive synthetic and real data experiments demonstrate that our methods outperform previous baseline methods on the tasks of tree topology probability estimation as well as Bayesian phylogenetic inference using SBNs.