论文标题
改进的变异贝叶斯系统发育推断和标准化流量
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
论文作者
论文摘要
变异贝叶斯系统发育推断（VBPI）提供了一个有希望的一般变异框架，可有效估计系统发育后期。但是，当前的对角对数正态分支长度近似将显着限制近似分布的质量。在本文中，我们提出了一种新型的VBPI VBPI-NF，作为通过深度学习技术赋予系统发育后验估计的第一步。通过处理具有精心设计的置换模型变换的系统发育模型的非欧亚人分支长度空间，VBPI-NF使用归一化的流量来提供丰富的柔性分支长度分布的家族，这些分布遍布不同的树拓扑。我们表明，VBPI-NF在挑战实际数据贝叶斯系统发育推断问题的基准上对Vanilla VBPI有了显着改善。进一步的研究还表明，这些置换模棱两可转换中的结构化参数化可以提供额外的摊销益处。
Variational Bayesian phylogenetic inference (VBPI) provides a promising general variational framework for efficient estimation of phylogenetic posteriors. However, the current diagonal Lognormal branch length approximation would significantly restrict the quality of the approximating distributions. In this paper, we propose a new type of VBPI, VBPI-NF, as a first step to empower phylogenetic posterior estimation with deep learning techniques. By handling the non-Euclidean branch length space of phylogenetic models with carefully designed permutation equivariant transformations, VBPI-NF uses normalizing flows to provide a rich family of flexible branch length distributions that generalize across different tree topologies. We show that VBPI-NF significantly improves upon the vanilla VBPI on a benchmark of challenging real data Bayesian phylogenetic inference problems. Further investigation also reveals that the structured parameterization in those permutation equivariant transformations can provide additional amortization benefit.
