Title: Improving Tree Probability Estimation with Stochastic Optimization and Variance Reduction Show Chinese title

Title: 通过随机优化和方差减少改进树概率估计

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. Show Chinese abstract

Abstract: 树拓扑的概率估计是系统发育推断中的基本任务之一。最近提出的子分割贝叶斯网络（SBNs）通过适当利用系统发育树的层次结构，为树拓扑概率估计提供了一个强大的概率图模型。然而，目前用于学习SBN参数的期望最大化（EM）方法无法扩展到大规模数据集。在本文中，我们引入了几种计算高效的SBN训练方法，并表明方差减少可能是提高性能的关键。此外，我们还将方差减少技术引入到变分贝叶斯系统发育推断（VBPI）的SBN参数优化中。大量的合成和真实数据实验表明，我们的方法在树拓扑概率估计以及使用SBNs的贝叶斯系统发育推断任务中均优于之前的基线方法。