The Robinson-Foulds (RF) metric is widely used by biologists, linguists and chemists to quantify similarity between pairs of phylogenetic trees. The measure tallies the number of bipartition splits that occur in both trees—but this conservative approach ignores potential similarities between almost-identical splits, with undesirable consequences. ‘Generalized’ RF metrics address this shortcoming by pairing splits in one tree with similar splits in the other. Each pair is assigned a similarity score, the sum of which enumerates the similarity between two trees. The challenge lies in quantifying split similarity: existing definitions lack a principled statistical underpinning, resulting in misleading tree distances that are difficult to interpret. Here, I propose probabilistic measures of split similarity, which allow tree similarity to be measured in natural units (bits).

中文翻译：

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信息理论用于比较系统树的广义Robinson-Foulds度量。

生物学家，语言学家和化学家广泛使用Robinson-Foulds（RF）度量来量化成对的系统树之间的相似性。该方法计算了两棵树中发生的分割分割的数量，但是这种保守的方法忽略了几乎完全相同的分割之间可能存在的相似性，从而产生了不良后果。“通用” RF指标通过将一棵树中的拆分与另一棵树中的相似拆分配对来解决此缺点。每对被分配一个相似度分数，其总和枚举了两棵树之间的相似度。挑战在于量化分割相似度：现有定义缺乏原则上的统计基础，从而导致难以解释的误导树距离。在此，我提出了分裂相似度的概率测度，