# Yule–Simon distribution

Parameters Probability mass function Yule–Simon PMF on a log-log scale. (Note that the function is only defined at integer values of k. The connecting lines do not indicate continuity.) Cumulative distribution function Yule–Simon CMF. (Note that the function is only defined at integer values of k. The connecting lines do not indicate continuity.) $\rho>0\,$ shape (real) $k \in \{1,2,\dots\}\,$ $\rho\,\mathrm{B}(k, \rho+1)\,$ $1 - k\,\mathrm{B}(k, \rho+1)\,$ $\frac{\rho}{\rho-1}\,$ for $\rho>1\,$ $1\,$ $\frac{\rho^2}{(\rho-1)^2\;(\rho-2)}\,$ for $\rho>2\,$ $\frac{(\rho+1)^2\;\sqrt{\rho-2}}{(\rho-3)\;\rho}\,$ for $\rho>3\,$ $\rho+3+\frac{11\rho^3-49\rho-22} {(\rho-4)\;(\rho-3)\;\rho}\,$ for $\rho>4\,$ $\frac{\rho}{\rho+1}\;{}_2F_1(1,1; \rho+2; e^t)\,e^t \,$ $\frac{\rho}{\rho+1}\;{}_2F_1(1,1; \rho+2; e^{i\,t})\,e^{i\,t} \,$

In probability and statistics, the Yule–Simon distribution is a discrete probability distribution named after Udny Yule and Herbert A. Simon. Simon originally called it the Yule distribution.[1]

The probability mass function of the Yule–Simon (ρ) distribution is

$f(k;\rho) = \rho\,\mathrm{B}(k, \rho+1), \,$

for integer $k \geq 1$ and real $\rho > 0$, where $\mathrm{B}$ is the beta function. Equivalently the pmf can be written in terms of the falling factorial as

$f(k;\rho) = \frac{\rho\,\Gamma(\rho+1)}{(k+\rho)^{\underline{\rho+1}}} , \,$

where $\Gamma$ is the gamma function. Thus, if $\rho$ is an integer,

$f(k;\rho) = \frac{\rho\,\rho!\,(k-1)!}{(k+\rho)!} . \,$

The parameter $\rho$ can be estimated using a fixed point algorithm.[2]

The probability mass function f has the property that for sufficiently large k we have

$f(k;\rho) \approx \frac{\rho\,\Gamma(\rho+1)}{k^{\rho+1}} \propto \frac{1}{k^{\rho+1}} . \,$

This means that the tail of the Yule–Simon distribution is a realization of Zipf's law: $f(k;\rho)$ can be used to model, for example, the relative frequency of the $k$th most frequent word in a large collection of text, which according to Zipf's law is inversely proportional to a (typically small) power of $k$.

## Occurrence

The Yule–Simon distribution arose originally as the limiting distribution of a particular stochastic process studied by Yule as a model for the distribution of biological taxa and subtaxa.[3] Simon dubbed this process the "Yule process" but it is more commonly known today as a preferential attachment process.[citation needed] The preferential attachment process is an urn process in which balls are added to a growing number of urns, each ball being allocated to an urn with probability linear in the number the urn already contains.

The distribution also arises as a compound distribution, in which the parameter of a geometric distribution is treated as a function of random variable having an exponential distribution.[citation needed] Specifically, assume that $W$ follows an exponential distribution with scale $1/\rho$ or rate $\rho$:

$W \sim \mathrm{Exponential}(\rho)\,,$

with density

$h(w;\rho) = \rho \, \exp(-\rho\,w)\, .$

Then a Yule–Simon distributed variable K has the following geometric distribution conditional on W:

$K \sim \mathrm{Geometric}(\exp(-W))\, .$

The pmf of a geometric distribution is

$g(k; p) = p \, (1-p)^{k-1}\,$

for $k\in\{1,2,\dots\}$. The Yule–Simon pmf is then the following exponential-geometric compound distribution:

$f(k;\rho) = \int_0^{\infty} \,\,\, g(k;\exp(-w))\,h(w;\rho)\,dw \, .$

## Generalizations

The two-parameter generalization of the original Yule distribution replaces the beta function with an incomplete beta function. The probability mass function of the generalized Yule–Simon(ρ, α) distribution is defined as

$f(k;\rho,\alpha) = \frac{\rho}{1-\alpha^{\rho}} \; \mathrm{B}_{1-\alpha}(k, \rho+1) , \,$

with $0 \leq \alpha < 1$. For $\alpha = 0$ the ordinary Yule–Simon(ρ) distribution is obtained as a special case. The use of the incomplete beta function has the effect of introducing an exponential cutoff in the upper tail.