使用混合模型改进变分贝叶斯系统发育推断
Improved Variational Bayesian Phylogenetic Inference using Mixtures
AI总结：
针对现有VBPI框架难以处理树拓扑后验多模态的问题，提出结合BBVI领域混合学习进展的VBPI-Mixtures算法，可建模VBPI无法捕捉的树拓扑分布，在多个真实系统发育数据集密度估计任务上达SOTA。
摘要：
我们提出VBPI-Mixtures算法，旨在提升系统发育后验分布的准确性，尤其针对树拓扑和分支长度的近似。尽管作为前沿黑盒变分推断（BBVI）框架的变分贝叶斯系统发育推断（VBPI）已能对这些分布实现出色近似，但树拓扑后验的多模态特性给BBVI这类基于采样的学习技术带来了巨大挑战。现有研究已探索用归一化流、图神经网络等先进深度学习方法优化分支长度后验近似，但改善树拓扑后验近似的研究仍较为匮乏。我们提出的新型VBPI-Mixtures算法利用BBVI领域混合学习的最新突破填补了这一空白，能够捕捉VBPI无法建模的树拓扑分布。我们在多个真实系统发育数据集的高难度密度估计任务上取得了当前最优性能。
英文摘要：
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.
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。
