We present VBPI-Mixtures, an algorithm designed to enhance the accuracy of phylogenetic posterior distributions, particularly for tree-topology and branch-length approximations. Despite the Variational Bayesian Phylogenetic Inference (VBPI), a leading-edge black-box variational inference (BBVI) framework, achieving remarkable approximations of these distributions, the multimodality of the tree-topology posterior presents a formidable challenge to sampling-based learning techniques such as BBVI. Advanced deep learning methodologies such as normalizing flows and graph neural networks have been explored to refine the branch-length posterior approximation, yet efforts to ameliorate the posterior approximation over tree topologies have been lacking. Our novel VBPI-Mixtures algorithm bridges this gap by harnessing the latest breakthroughs in mixture learning within the BBVI domain. As a result, VBPI-Mixtures is capable of capturing distributions over tree-topologies that VBPI fails to model. We deliver state-of-the-art performance on difficult density estimation tasks across numerous real phylogenetic datasets.

翻译：本文提出VBPI-Mixtures算法，旨在提升系统发育后验分布的精度，特别是针对树拓扑和分支长度近似。尽管变分贝叶斯系统发育推断（VBPI）作为领先的黑箱变分推断（BBVI）框架，已能出色地近似这些分布，但树拓扑后验的多模态性对基于采样的学习技术（如BBVI）构成了严峻挑战。已有研究探索了归一化流和图神经网络等先进深度学习方法以改进分支长度后验近似，然而针对树拓扑后验近似优化的努力仍显不足。我们提出的新型VBPI-Mixtures算法通过利用BBVI领域混合学习的最新突破填补了这一空白，能够捕获VBPI无法建模的树拓扑分布。在多个真实生物数据集上的困难密度估计任务中，我们取得了最优性能。