English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Zipf's law, which states that the probability of an observation is inverselyproportional to its rank, has been observed in many domains. While there aremodels that explain Zipf's law in each of them, those explanations aretypically domain specific. Recently, methods from statistical physics were usedto show that a fairly broad class of models does provide a general explanationof Zipf's law. This explanation rests on the observation that real world datais often generated from underlying causes, known as latent variables. Thoselatent variables mix together multiple models that do not obey Zipf's law,giving a model that does. Here we extend that work both theoretically andempirically. Theoretically, we provide a far simpler and more intuitiveexplanation of Zipf's law, which at the same time considerably extends theclass of models to which this explanation can apply. Furthermore, we also givemethods for verifying whether this explanation applies to a particular dataset.Empirically, these advances allowed us extend this explanation to importantclasses of data, including word frequencies (the first domain in which Zipf'slaw was discovered), data with variable sequence length, and multi-neuronspiking activity.

Zipf's law arises naturally in structured, high-dimensional data