利用随机优化与方差缩减改进树概率估计
Improving Tree Probability Estimation with Stochastic Optimization and Variance Reduction
AI总结：
针对子分裂贝叶斯网络参数学习中EM难以扩展的问题，提出高效随机训练方法并引入方差缩减，同时改进VBPI中的参数优化，实验显示在树拓扑概率估计和贝叶斯系统发育推断上均优于基线。
摘要：
树拓扑的概率估计是系统发育推断中的基本任务之一。最近提出的子分裂贝叶斯网络（SBN）通过恰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种强大的概率图模型。然而，当前用于学习SBN参数的期望最大化（EM）方法无法扩展到大型数据集。本文介绍了若干用于训练SBN的计算高效方法，并表明方差缩减可能是获得更优性能的关键。此外，我们还引入方差缩减技术，以改进变分贝叶斯系统发育推断（VBPI）中SBN参数的优化。大量合成数据与真实数据实验表明，在利用SBN进行树拓扑概率估计以及贝叶斯系统发育推断的任务上，我们的方法优于以往的基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
