Improving Tree Probability Estimation with Stochastic Optimization and Variance Reduction

• URL: http://arxiv.org/abs/2409.05282v1
• Date: Mon, 9 Sep 2024 02:22:52 GMT
• Title: Improving Tree Probability Estimation with Stochastic Optimization and Variance Reduction
• Authors: Tianyu Xie, Musu Yuan, Minghua Deng, Cheng Zhang,
• Abstract summary: Subsplit Bayesian networks (SBNs) provide a powerful probabilistic graphical model for tree probability estimation. The expectation (EM) method currently used for learning SBN parameters does not scale up to large data sets. We introduce several computationally efficient methods for training SBNs and show that variance reduction could be the key for better performance.
• Score: 11.417249588622926
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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