提出可微分变分推断方法，高效精确估算进化树后验分布。
Variational phylogenetic inference with products over bipartitions
- 基于单链接聚类的共祖时间构造新变分族
- 在基准基因组数据上精度媲美顶尖方法，梯度计算量更少
- 适合需要快速迭代的进化分析与可微分建模场景
贝叶斯系统发育学对理解演化动态至关重要，需对树结构的后验分布进行准确高效的近似。本文提出一种针对超时标进化树的变分贝叶斯方法。我们基于单链接聚类的共祖时间构建新颖的变分族，并推导出树空间上的闭式密度表达。与现有超时标树方法不同，本方法可在整个树空间上进行推断，无需任何马尔可夫链蒙特卡洛子程序，且变分族具备可微性。在基准基因组数据集及新冠病毒RNA的应用实验中，本方法在保持竞争性精度的同时，显著减少了所需梯度评估次数，优于当前最先进技术。
原文摘要 · Abstract (English)
Bayesian phylogenetics is vital for understanding evolutionary dynamics, and requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form density for the resulting distribution over trees. Unlike existing methods for ultrametric trees, our method performs inference over all of tree space, it does not require any Markov chain Monte Carlo subroutines, and our variational family is differentiable. Through experiments on benchmark genomic datasets and an application to the viral RNA of SARS-CoV-2, we demonstrate that our method achieves competitive accuracy while requiring significantly fewer gradient evaluations than existing state-of-the-art techniques.
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