arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1104.1789physics.soc-phcs.SI
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齐普夫定律解开了

Zipf's law unzipped

Seung Ki Baek, Sebastian Bernhardsson, Petter Minnhagen

摘要:

为什么齐普夫定律能很好地描述看似完全不相关的现象的数据?这里有人认为,原因是它们都可以被描述为无处不在的随机群体划分的结果:元素可以是一个国家的公民和群体的姓氏,或者元素可以是构成小说的所有单词和群体的唯一单词,或者元素可以是一个国家的居民和群体的城市,提出了一种随机组形成(RGF),从该随机组形成(RGF)中基于最小信息获得贝叶斯估计:在给定元素、组的总数和最大组中的元素的数目的情况下,它提供了对具有$k$元素的组的数目的最佳预测。对于这三个值的每个规范,RGF预测唯一的组分布$N(k)\propto \exp(-bk)/k^γ$,其中幂律指数$γ$是相同三个值的唯一函数。结果的普遍性是可能的事实,即没有系统特定的假设负责组划分的机制。计算$γ$与元素、组的总数以及最大组中的元素数之间的直接关系。通过与来自各种系统的数据直接比较,证明了RGF模型的预测能力。它示出,$γ$通常采取的值在间隔$1\leqγ\leq 2$和一个给定的现象的值取决于在一个系统的方式上的数据集的总大小。结果是把在Zipf的和Gibrat的法律,$N(k)\propto k^{-2}$和增长模型和RGF之间的连接的早期讨论的背景下阐明。

英文摘要:

Why does Zipf's law give a good description of data from seemingly completely unrelated phenomena? Here it is argued that the reason is that they can all be described as outcomes of a ubiquitous random group division: the elements can be citizens of a country and the groups family names, or the elements can be all the words making up a novel and the groups the unique words, or the elements could be inhabitants and the groups the cities in a country, and so on. A Random Group Formation (RGF) is presented from which a Bayesian estimate is obtained based on minimal information: it provides the best prediction for the number of groups with $k$ elements, given the total number of elements, groups, and the number of elements in the largest group. For each specification of these three values, the RGF predicts a unique group distribution $N(k)\propto \exp(-bk)/k^γ$, where the power-law index $γ$ is a unique function of the same three values. The universality of the result is made possible by the fact that no system specific assumptions are made about the mechanism responsible for the group division. The direct relation between $γ$ and the total number of elements, groups, and the number of elements in the largest group, is calculated. The predictive power of the RGF model is demonstrated by direct comparison with data from a variety of systems. It is shown that $γ$ usually takes values in the interval $1\leqγ\leq 2$ and that the value for a given phenomena depends in a systematic way on the total size of the data set. The results are put in the context of earlier discussions on Zipf's and Gibrat's laws, $N(k)\propto k^{-2}$ and the connection between growth models and RGF is elucidated.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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