论文标题
合并和重复的联合建模下的物种树木估计：四重奏方法的样本复杂性
Species tree estimation under joint modeling of coalescence and duplication: sample complexity of quartet methods
论文作者
论文摘要
我们考虑基因树进化的标准随机模型下的物种树估计，该模型融合了不完整的谱系分类（通过合并过程建模）以及基因的重复和损失（由分支过程建模）。通过对模型的概率分析，我们为广泛使用的基于四重奏的推理方法得出了样本复杂性界限，这些方法突出了亚临界和超临界方案的重复和损失率的效果。
We consider species tree estimation under a standard stochastic model of gene tree evolution that incorporates incomplete lineage sorting (as modeled by a coalescent process) and gene duplication and loss (as modeled by a branching process). Through a probabilistic analysis of the model, we derive sample complexity bounds for widely used quartet-based inference methods that highlight the effect of the duplication and loss rates in both subcritical and supercritical regimes.
