Zeta distribution

The Zeta distribution is defined for positive integers ${\displaystyle k\geq 1}$, and its probability mass function is given by

${\displaystyle P(x=k)={\frac {1}{\zeta (s)}}k^{-s}}$,

where ${\displaystyle s>1}$ is the parameter, and ${\displaystyle \zeta (s)}$ is the Riemann zeta function.

The cumulative distribution function is given by

${\displaystyle P(x\leq k)={\frac {H_{k,s}}{\zeta (s)}},}$

where ${\displaystyle H_{k,s}}$ is the generalized harmonic number

${\displaystyle H_{k,s}=\sum _{i=1}^{k}{\frac {1}{i^{s}}}.}$

The nth raw moment is defined as the expected value of Xⁿ:

${\displaystyle m_{n}=E(X^{n})={\frac {1}{\zeta (s)}}\sum _{k=1}^{\infty }{\frac {1}{k^{s-n}}}}$

The series on the right is just a series representation of the Riemann zeta function, but it only converges for values of ${\displaystyle s-n}$ that are greater than unity. Thus:

${\displaystyle m_{n}=\left\{{\begin{matrix}\zeta (s-n)/\zeta (s)&{\textrm {for}}~n<s-1\\\infty &{\textrm {for}}~n\geq s-1\end{matrix}}\right.}$

Note that the ratio of the zeta functions is well defined, even for n > s − 1 because the series representation of the zeta function can be analytically continued. This does not change the fact that the moments are specified by the series itself, and are therefore undefined for large n.

ζ(1) is infinite as the harmonic series, and so the case when s = 1 is not meaningful. However, if A is any set of positive integers that has a density, i.e. if

${\displaystyle \lim _{n\to \infty }{\frac {N(A,n)}{n}}}$

exists where N(A, n) is the number of members of A less than or equal to n, then

${\displaystyle \lim _{s\to 1^{+}}P(X\in A)\,}$

is equal to that density.

The latter limit can also exist in some cases in which A does not have a density. For example, if A is the set of all positive integers whose first digit is d, then A has no density, but nonetheless the second limit given above exists and is proportional to

${\displaystyle \log(d+1)-\log(d)=\log \left(1+{\frac {1}{d}}\right),\,}$

which is Benford's law.

The Zeta distribution can be constructed with a sequence of independent random variables with a Geometric distribution. Let ${\displaystyle p}$ be a prime number and ${\displaystyle X(p^{-s})}$ be a random variable with a Geometric distribution of parameter ${\displaystyle p^{-s}}$, namely

${\displaystyle \quad \quad \quad \mathbb {P} \left(X(p^{-s})=k\right)=p^{-ks}(1-p^{-s})}$

If the random variables ${\displaystyle (X(p^{-s}))_{p\in {\mathcal {P}}}}$ are independent, then, the random variable ${\displaystyle Z_{s}}$ defined by

${\displaystyle \quad \quad \quad Z_{s}=\prod _{p\in {\mathcal {P}}}p^{X(p^{-s})}}$

has the Zeta distribution : ${\displaystyle \mathbb {P} \left(Z_{s}=n\right)={\frac {1}{n^{s}\zeta (s)}}}$.

Stated differently, the random variable ${\displaystyle \log(Z_{s})=\sum _{p\in {\mathcal {P}}}X(p^{-s})\,\log(p)}$ is infinitely divisible with Lévy measure given by the following sum of Dirac masses :

${\displaystyle \quad \quad \quad \Pi _{s}(dx)=\sum _{p\in {\mathcal {P}}}\sum _{k\geqslant 1}{\frac {p^{-ks}}{k}}\delta _{k\log(p)}(dx)}$

Other "power-law" distributions

• Cauchy distribution
• Lévy distribution
• Lévy skew alpha-stable distribution
• Pareto distribution
• Zipf's law
• Zipf–Mandelbrot law
• Infinitely divisible distribution

• Gut, Allan. "Some remarks on the Riemann zeta distribution". CiteSeerX 10.1.1.66.3284. Cite journal requires |journal= (help) What Gut calls the "Riemann zeta distribution" is actually the probability distribution of −log X, where X is a random variable with what this article calls the zeta distribution.
• Weisstein, Eric W. "Zipf Distribution". MathWorld.