mlsanityData to 14 Oct 2026

Big Data and Large Numbers. Interpreting Zipf's Law

arXiv 2305.02687 ↗First posted 4 May 2023physics.soc-ph cs.CL math.STcited by 0

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Abstract (arXiv)

It turns out that some empirical facts in Big Data are the effects of properties of large numbers. Zipf's law 'noise' is an example of such an artefact. We expose several properties of the power law distributions and of similar distribution that occur when the population is finite and the rank and counts of elements in the population are natural numbers. We are particularly concerned with the low-rank end of the graph of the law, the potential of noise in the law, and with the approximation of the number of types of objects at various ranks. Approximations instead of exact solutions are the center of attention. Consequences in the interpretation of Zipf's law are discussed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 27 references that are in the map. First posted on arXiv 4 May 2023.

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References · 3

  1. 2014Zipf's law holds for phrases, not wordscited by 12
  2. 2014Zipf's law for word frequencies: word forms versus lemmas in long textscited by 6
  3. 2019Bayesian inferences on uncertain ranks and orderings: Application to ranking players and lineupscited by 2

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