ArticlePLoS computational biology2016
Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality.Scientific reports · 2024Article
- The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality.bioRxiv : the preprint server for biology · 2024Article
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- Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Scaling Laws for Phonotactic Complexity in Spoken English Language Data.Language and speech · 2021Article
- Empirical evidence for concerted evolution in the 18S rDNA region of the planktonic diatom genus Chaetoceros.Scientific reports · 2021Article
- Short- and Long-Range Connections Differentially Modulate the Dynamics and State of Small-World Networks.Frontiers in computational neuroscience · 2021Article
- Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system.PLoS biology · 2020Article
- Article
- Optimal Encoding in Stochastic Latent-Variable Models.Entropy (Basel, Switzerland) · 2020Article
- The Brevity Law as a Scaling Law, and a Possible Origin of Zipf's Law for Word Frequencies.Entropy (Basel, Switzerland) · 2020Article
- A high-bias, low-variance introduction to Machine Learning for physicists.Physics reports · 2019Article
- Can a time varying external drive give rise to apparent criticality in neural systems?PLoS computational biology · 2018Article
- RepSeq Data Representativeness and Robustness Assessment by Shannon Entropy.Frontiers in immunology · 2018Article
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Authors and funding
3 authors.
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Abstract
Zipf's law, which states that the probability of an observation is inversely proportional to its rank, has been observed in many domains. While there are models that explain Zipf's law in each of them, those explanations are typically domain specific. Recently, methods from statistical physics were used to show that a fairly broad class of models does provide a general explanation of Zipf's law. This explanation rests on the observation that real world data is often generated from underlying causes, known as latent variables. Those latent variables mix together multiple models that do not obey Zipf's law, giving a model that does. Here we extend that work both theoretically and empirically. Theoretically, we provide a far simpler and more intuitive explanation of Zipf's law, which at the same time considerably extends the class of models to which this explanation can apply. Furthermore, we also give methods for verifying whether this explanation applies to a particular dataset. Empirically, these advances allowed us extend this explanation to important classes of data, including word frequencies (the first domain in which Zipf's law was discovered), data with variable sequence length, and multi-neuron spiking activity.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.