论文标题
贝叶斯系统发育推断双曲线空间的保真度
Fidelity of Hyperbolic Space for Bayesian Phylogenetic Inference
论文作者
论文摘要
贝叶斯对系统发育学的推断是计算系统发育分布的金标准。它面临着具有挑战性的问题。在树木的高维空间中移动。但是，双曲线空间提供了类似树状数据的低维表示。在本文中，我们将基因组序列嵌入双曲线空间中，并对贝叶斯推断进行双曲线马尔可夫链蒙特卡洛。后验概率是通过解码从建议的嵌入位置的邻居连接树来计算的。我们从经验上证明了这种方法在八个数据集上的保真度。采样的后验分布恢复了分裂和分支长度的高度。我们研究了曲率和嵌入维度对马尔可夫链性能的影响。最后，我们讨论了适应此方法以用梯度导航树空间的前景。
Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. It faces the challenging problem of. moving throughout the high-dimensional space of trees. However, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences into hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference. The posterior probability is computed by decoding a neighbour joining tree from proposed embedding locations. We empirically demonstrate the fidelity of this method on eight data sets. The sampled posterior distribution recovers the splits and branch lengths to a high degree. We investigated the effects of curvature and embedding dimension on the Markov Chain's performance. Finally, we discuss the prospects for adapting this method to navigate tree space with gradients.
