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A Generalization of the Erd\H{o}s-Kac Theorem

Number Theory 2020-11-03 v1 Probability

Abstract

Given nNn\in\mathbb{N}, let ω(n)\omega\left(n\right) denote the number of distinct prime factors of nn, let ZZ denote a standard normal variable, and let PnP_{n} denote the uniform distribution on {1,,n}\left\{ 1,\ldots,n\right\} . The Erd\H{o}s-Kac Theorem states that Pn(mn:ω(m)loglognx(loglogn)1/2)P(Zx)P_{n}\left(m\le n:\omega\left(m\right)-\log\log n\le x\left(\log\log n\right)^{1/2}\right)\to\mathbb{P}\left(Z\le x\right) as nn\to\infty; i.e., if N(n)N\left(n\right) is a uniformly distributed variable on {1,,n}\lbrace 1,\ldots,n \rbrace, then ω(N(n))\omega\left(N\left(n\right)\right) is asymptotically normally distributed as nn\to \infty with both mean and variance equal to loglogn\log \log n. The contribution of this paper is a generalization of the Erd\H{o}s-Kac Theorem to a larger class of random variables by considering perturbations of the uniform probability mass 1n\frac{1}{n} in the following sense. Denote by Pn\mathbb{P}_{n} a probability distribution on {1,,n}\left\{ 1,\ldots,n\right\} given by Pn(i)=1n+εi,n\mathbb{P}_{n}\left(i\right)=\frac{1}{n}+\varepsilon_{i,n}. By providing some constraints on the εi,n\varepsilon_{i,n}'s, sufficient conditions are stated in order to conclude that Pn(mn:ω(m)loglognx(loglogn)1/2)P(Zx)\mathbb{P}_{n}\left(m\le n:\omega\left(m\right)-\log\log n\le x\left(\log\log n\right)^{1/2}\right) \to \mathbb{P}\left(Z\le x\right) as n.n\to\infty. The main result will be applied to prove that the number of distinct prime factors of a positive integer with either the Harmonic(n)\left(n\right) distribution or the Zipf(n,s)\left(n,s\right) distribution also tends to the normal distribution N(loglogn,loglogn)\mathcal{N}\left(\log\log n,\log\log n\right) as nn\to\infty (and as s1s\to1 in the case of a Zipf variable).

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Cite

@article{arxiv.2011.00152,
  title  = {A Generalization of the Erd\H{o}s-Kac Theorem},
  author = {Joseph Squillace},
  journal= {arXiv preprint arXiv:2011.00152},
  year   = {2020}
}

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