Berling Lars, Klawitter Jonathan, Bouckaert Remco, Xie Dong, Gavryushkin Alex, Drummond Alexei J
School of Mathematics and Statistics, University of Canterbury, Aotearoa, New Zealand.
Biomathematics Research Centre, University of Canterbury, Aotearoa, New Zealand.
PLoS Comput Biol. 2025 Feb 13;21(2):e1012789. doi: 10.1371/journal.pcbi.1012789. eCollection 2025 Feb.
Bayesian phylogenetic analysis with MCMC algorithms generates an estimate of the posterior distribution of phylogenetic trees in the form of a sample of phylogenetic trees and related parameters. The high dimensionality and non-Euclidean nature of tree space complicates summarizing the central tendency and variance of the posterior distribution in tree space. Here we introduce a new tractable tree distribution and associated point estimator that can be constructed from a posterior sample of trees. Through simulation studies we show that this point estimator performs at least as well and often better than standard methods of producing Bayesian posterior summary trees. We also show that the method of summary that performs best depends on the sample size and dimensionality of the problem in non-trivial ways.
使用MCMC算法的贝叶斯系统发育分析以系统发育树样本和相关参数的形式生成系统发育树后验分布的估计值。树空间的高维度和非欧几里得性质使得总结树空间中后验分布的中心趋势和方差变得复杂。在这里，我们引入了一种新的易于处理的树分布和相关的点估计器，它可以从树的后验样本中构建。通过模拟研究，我们表明这种点估计器的性能至少与生成贝叶斯后验总结树的标准方法一样好，而且通常更好。我们还表明，表现最佳的总结方法以非平凡的方式取决于样本大小和问题的维度。
