Designing flexible probabilistic models over tree topologies is important for
developing efficient phylogenetic inference methods. To do that, previous works
often leverage the similarity of tree topologies via hand-engineered heuristic
features which would require pre-sampled tree topologies and may suffer from
limited approximation capability. In this paper, we propose a deep
autoregressive model for phylogenetic inference based on graph neural networks
(GNNs), called ARTree. By decomposing a tree topology into a sequence of leaf
node addition operations and modeling the involved conditional distributions
based on learnable topological features via GNNs, ARTree can provide a rich
family of distributions over the entire tree topology space that have simple
sampling algorithms and density estimation procedures, without using heuristic
features. We demonstrate the effectiveness and efficiency of our method on a
benchmark of challenging real data tree topology density estimation and
variational Bayesian phylogenetic inference problems.

我们提出了一种基于图神经网络的深度自回归模型 ARTree，可以灵活地设计概率模型并解决系统发生推断问题，通过学习可学习的拓扑特征来对树的拓扑结构进行建模，并提供了一系列包含简单采样算法和密度估计过程的树拓扑空间分布，无需使用启发式特征。我们在具有挑战性的真实数据树拓扑密度估计和变分贝叶斯系统发生推断问题的基准测试中证明了我们方法的效果和效率。