从树到条形码再回来II:拓扑逆问题的组合和概率方面

IF 0.4 4区 计算机科学 Q4 MATHEMATICS
Justin Curry, Jordan DeSha, Adélie Garin, Kathryn Hess, Lida Kanari, Brendan Mallery
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引用次数: 8

摘要

在本文中,我们考虑了如何构造实现给定条形码的合并树的反问题的两个方面。我们的大部分研究都利用了最近发现的对称群和一般位置的条形码之间的联系,基于死亡顺序是出生顺序的排列这一简单观察。我们展示了如何将条形码的这种组合特征提升为合并树的类似组合化。作为这项研究的结果,我们在系统发育树的空间(由Billera、Holmes和Vogtmann定义)和合并树的空间之间提供了第一个明确的组合区别:n+1个叶节点上的一般系统发育树属于(2n−1)!!不同的等价类,但合并树的类似数量等于分区格中最大链的数量,即(n+1)!n2−n。我们研究的第二个方面是,当我们假设条形码是使用对称群上的均匀分布进行采样时,推导出树实现数(实现给定条形码的合并树的数量)分布的精确公式。我们能够表征这种分布的一些高阶矩,这在一定程度上要归功于我们根据狄利克雷卷积对分布的重新表述。这种表征提供了一种类型的零假设,显然不同于在真实神经元数据中观察到的分布,这为进行更精确的统计和科学研究打开了大门。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem

In this paper we consider two aspects of the inverse problem of how to construct merge trees realizing a given barcode. Much of our investigation exploits a recently discovered connection between the symmetric group and barcodes in general position, based on the simple observation that death order is a permutation of birth order. We show how to lift this combinatorial characterization of barcodes to an analogous combinatorialization of merge trees. As result of this study, we provide the first clear combinatorial distinction between the space of phylogenetic trees (as defined by Billera, Holmes and Vogtmann) and the space of merge trees: generic phylogenetic trees on n+1 leaf nodes fall into (2n1)!! distinct equivalence classes, but the analogous number for merge trees is equal to the number of maximal chains in the lattice of partitions, i.e., (n+1)!n!2n. The second aspect of our study is the derivation of precise formulas for the distribution of tree realization numbers (the number of merge trees realizing a given barcode) when we assume that barcodes are sampled using a uniform distribution on the symmetric group. We are able to characterize some of the higher moments of this distribution, thanks in part to a reformulation of our distribution in terms of Dirichlet convolution. This characterization provides a type of null hypothesis, apparently different from the distributions observed in real neuron data, which opens the door to doing more precise statistics and science.

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来源期刊
CiteScore
1.60
自引率
16.70%
发文量
43
审稿时长
>12 weeks
期刊介绍: Computational Geometry is a forum for research in theoretical and applied aspects of computational geometry. The journal publishes fundamental research in all areas of the subject, as well as disseminating information on the applications, techniques, and use of computational geometry. Computational Geometry publishes articles on the design and analysis of geometric algorithms. All aspects of computational geometry are covered, including the numerical, graph theoretical and combinatorial aspects. Also welcomed are computational geometry solutions to fundamental problems arising in computer graphics, pattern recognition, robotics, image processing, CAD-CAM, VLSI design and geographical information systems. Computational Geometry features a special section containing open problems and concise reports on implementations of computational geometry tools.
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