On exchangeable random variables and the statistics of large graphs and hypergraphs

IF 1.3 Q2 STATISTICS & PROBABILITY
Tim Austin
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引用次数: 127

Abstract

De Finetti’s classical result of [18] identifying the law of an exchangeable family of random variables as a mixture of i.i.d. laws was extended to structure theorems for more complex notions of exchangeability by Aldous [1, 2, 3], Hoover [41, 42], Kallenberg [44] and Kingman [47]. On the other hand, such exchangeable laws were first related to questions from combinatorics in an independent analysis by Fremlin and Talagrand [29], and again more recently in Tao [62], where they appear as a natural proxy for the ‘leading order statistics’ of colourings of large graphs or hypergraphs. Moreover, this relation appears implicitly in the study of various more bespoke formalisms for handling ‘limit objects’ of sequences of dense graphs or hypergraphs in a number of recent works, including Lovasz and Szegedy [52], Borgs, Chayes, Lovasz, Sos, Szegedy and Vesztergombi [17], Elek and Szegedy [24] and Razborov [54, 55]. However, the connection between these works and the earlier probabilistic structural results seems to have gone largely unappreciated. In this survey we recall the basic results of the theory of exchangeable laws, and then explain the probabilistic versions of various interesting questions from graph and hypergraph theory that their connection motivates (particularly extremal questions on the testability of properties for graphs and hypergraphs). We also locate the notions of exchangeability of interest to us in the context of other classes of probability measures subject to various symmetries, in particular contrasting the methods employed to analyze exchangeable laws with related structural results in ergodic theory, particular the Furstenberg-Zimmer structure theorem for probability-preserving ℤ-systems, which underpins Furstenberg’s ergodic-theoretic proof of Szemeredi’s Theorem. The forthcoming paper [10] will make a much more elaborate appeal to the link between exchangeable laws and dense (directed) hypergraphs to establish various results in property testing.
交换随机变量与大图和超图的统计
De Finetti的经典结果[18]将可交换随机变量族的规律识别为i.i.d定律的混合物,并被Aldous[1,2,3]、Hoover[41,42]、Kallenberg[44]和Kingman[47]扩展为更复杂的可交换性概念的结构定理。另一方面,这些可交换律首先与Fremlin和Talagrand bbb的独立分析中的组合学问题有关,最近在Tao[62]中再次出现,在那里它们作为大图或超图着色的“领先阶统计量”的自然代理。此外,在最近的一些研究中,这种关系隐含地出现在处理密集图或超图序列的“极限对象”的各种定制形式的研究中,包括Lovasz和Szegedy [52], Borgs, Chayes, Lovasz, Sos, Szegedy和Vesztergombi [17], Elek和Szegedy[24]和Razborov[54,55]。然而,这些工作与早期的概率结构结果之间的联系似乎在很大程度上没有得到重视。在这篇综述中,我们回顾了交换定律理论的基本结果,然后解释了图和超图理论中由它们的联系引起的各种有趣问题的概率版本(特别是关于图和超图性质的可测试性的极端问题)。我们还将我们感兴趣的可交换性概念定位在其他类别的概率度量中,这些概率度量受各种对称性的影响,特别是将用于分析可交换律的方法与遍历理论中的相关结构结果进行对比,特别是用于概率保持的v系统的Furstenberg- zimmer结构定理,它支撑了Furstenberg对Szemeredi定理的遍历理论证明。即将发表的论文[10]将对交换定律和密集(定向)超图之间的联系做出更详细的呼吁,以建立性质测试中的各种结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Probability Surveys
Probability Surveys STATISTICS & PROBABILITY-
CiteScore
4.70
自引率
0.00%
发文量
9
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