Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history and numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods.

采用生成流网络的框架，本文研究在系统发生学中的两个核心问题：基于减少原则和贝叶斯方法的系统发生学推断。通过我们的前向后验采样器，PhyloGFN，我们展示了在真实基准数据集上产生多样化和高质量的进化假设。PhyloGFN在边缘似然度估计方面与现有工作相媲美，并实现了更接近目标分布的拟合效果，超过了最先进的变分推断方法。

PhyloGFN: 基于生成流网络的系统发育推断