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Families of Monotonic Trees: Combinatorial Enumeration and Asymptotics

机译:单调树的家庭:组合枚举和渐近性

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There exists a wealth of literature concerning families of increasing trees, particularly suitable for representing the evolution of either data structures in computer science, or probabilistic urns in mathematics, but are also adapted to model evolutionary trees in biology. The classical notion of increasing trees corresponds to labeled trees such that, along paths from the root to any leaf, node labels are strictly increasing; in addition nodes have distinct labels. In this paper we introduce new families of increasingly labeled trees relaxing the constraint of unicity of each label. Such models are especially useful to characterize processes evolving in discrete time whose nodes evolve simultaneously. In particular, we obtain growth processes for biology much more adequate than the previous increasing models. The families of monotonic trees we introduce are much more delicate to deal with, since they are not decomposable in the sense of Analytic Combinatorics. New tools are required to study the quantitative statistics of such families. In this paper, we first present a way to combinatorially specify such families through evolution processes, then, we study the tree enumerations.
机译:有大量有关树木生长家族的文献,特别适合于代表计算机科学中的数据结构或数学中的概率缸的演化,但也适用于对生物学中的进化树进行建模。树木增加的经典概念与带标签的树木相对应,这样,沿着从根到任何叶子的路径,节点标签都在严格增加;此外,节点具有不同的标签。在本文中,我们介绍了标签不断增多的树木的新家族,这些树木减轻了每个标签的唯一性的限制。这样的模型对于表征离散时间演变的进程(其节点同时演化)特别有用。特别是,我们获得的生物学生长过程比以前的增长模型更加充分。我们引入的单调树家族要处理起来要微妙得多,因为它们在分析组合学的意义上是不可分解的。需要新的工具来研究此类家庭的定量统计数据。在本文中,我们首先提出一种通过进化过程来组合指定此类族的方法,然后研究树枚举。

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