# Discrete-stable distribution

The discrete-stable distributions[1] are a class of probability distributions with the property that the sum of several random variables from such a distribution is distributed according to the same family. They are the discrete analogue of the continuous-stable distributions.

The discrete-stable distributions have been used in numerous fields, in particular in scale-free networks such as the internet, social networks[2] or even semantic networks[3]

Both the discrete and continuous classes of stable distribution have properties such as infinitely divisibility, power law tails and unimodality.

The most well-known discrete stable distribution is the Poisson distribution which is a special case as the only discrete-stable distribution for which the mean and all higher-order moments are finite[dubious ].

## Definition

The discrete-stable distributions are defined[4] through their probability-generating function

${\displaystyle G(s|\nu ,a)=\sum _{n=0}^{\infty }P(N|\nu ,a)(1-s)^{N}=\exp(-as^{\nu }).}$

In the above, ${\displaystyle a>0}$ is a scale parameter and ${\displaystyle 0<\nu \leq 1}$ describes the power-law behaviour such that when ${\displaystyle 0<\nu <1}$,

${\displaystyle \lim _{N\to \infty }P(N|\nu ,a)\sim {\frac {1}{N^{\nu +1}}}.}$

When ${\displaystyle \nu =1}$ the distribution becomes the familiar Poisson distribution with mean ${\displaystyle a}$.

The original distribution is recovered through repeated differentiation of the generating function:

${\displaystyle P(N|\nu ,a)=\left.{\frac {(-1)^{N}}{N!}}{\frac {d^{N}G(s|\nu ,a)}{ds^{N}}}\right|_{s=1}.}$

A closed-form expression using elementary functions for the probability distribution of the discrete-stable distributions is not known except for in the Poisson case, in which

${\displaystyle \!P(N|\nu =1,a)={\frac {a^{N}e^{-a}}{N!}}.}$

Expressions do exist, however, using special functions for the case ${\displaystyle \nu =1/2}$[5] (in terms of Bessel functions) and ${\displaystyle \nu =1/3}$[6] (in terms of hypergeometric functions).

## As compound probability distributions

The entire class of discrete-stable distributions can be formed as Poisson compound probability distributions where the mean, ${\displaystyle \lambda }$, of a Poisson distribution is defined as a random variable with a probability density function (PDF). When the PDF of the mean is a one-sided continuous-stable distribution with stability parameter ${\displaystyle 0<\alpha <1}$ and scale parameter ${\displaystyle c}$ the resultant distribution is[7] discrete-stable with index ${\displaystyle \nu =\alpha }$ and scale parameter ${\displaystyle a=c\sec(\alpha \pi /2)}$.

Formally, this is written:

${\displaystyle P(N|\alpha ,c\sec(\alpha \pi /2))=\int _{0}^{\infty }P(N|1,\lambda )p(\lambda ;\alpha ,1,c,0)\,d\lambda }$

where ${\displaystyle p(x;\alpha ,1,c,0)}$ is the pdf of a one-sided continuous-stable distribution with symmetry paramètre ${\displaystyle \beta =1}$ and location parameter ${\displaystyle \mu =0}$.

A more general result[6] states that forming a compound distribution from any discrete-stable distribution with index ${\displaystyle \nu }$ with a one-sided continuous-stable distribution with index ${\displaystyle \alpha }$ results in a discrete-stable distribution with index ${\displaystyle \nu \cdot \alpha }$, reducing the power-law index of the original distribution by a factor of ${\displaystyle \alpha }$.

In other words,

${\displaystyle P(N|\nu \cdot \alpha ,c\sec(\pi \alpha /2)=\int _{0}^{\infty }P(N|\alpha ,\lambda )p(\lambda ;\nu ,1,c,0)\,d\lambda .}$

## In the Poisson limit

In the limit ${\displaystyle \nu \rightarrow 1}$, the discrete-stable distributions behave[7] like a Poisson distribution with mean ${\displaystyle a\sec(\nu \pi /2)}$ for small ${\displaystyle N}$, however for ${\displaystyle N\gg 1}$, the power-law tail dominates.

The convergence of i.i.d. random variates with power-law tails ${\displaystyle P(N)\sim 1/N^{1+\nu }}$ to a discrete-stable distribution is extraordinarily slow[8] when ${\displaystyle \nu \approx 1}$ - the limit being the Poisson distribution when ${\displaystyle \nu >1}$ and ${\displaystyle P(N|\nu ,a)}$ when ${\displaystyle \nu \leq 1}$.

## References

1. ^ Steutel, F. W.; van Harn, K. (1979). "Discrete Analogues of Self-Decomposability and Stability". Annals of Probability. 7 (5): 893–899. doi:10.1214/aop/1176994950.
2. ^ Barabási, Albert-László (2003). Linked: how everything is connected to everything else and what it means for business, science, and everyday life. New York, NY: Plum.
3. ^ Steyvers, M.; Tenenbaum, J. B. (2005). "The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth". Cognitive science. 29 (1): 41–78. doi:10.1207/s15516709cog2901_3.
4. ^ Hopcraft, K. I.; Jakeman, E.; Matthews, J. O. (2002). "Generation and monitoring of a discrete stable random process". Journal of Physics A: General Physics. 35 (49): L745–752. doi:10.1088/0305-4470/35/49/101.
5. ^ Matthews, J. O.; Hopcraft, K. I.; Jakeman, E. (2003). "Generation and monitoring of discrete stable random processes using multiple immigration population models". Journal of Physics A: Mathematical and General. 36: 11585–11603. doi:10.1088/0305-4470/36/46/004.
6. ^ a b Lee, W.H. (2010). Continuous and discrete properties of stochastic processes (PhD thesis). The University of Nottingham.
7. ^ a b Lee, W. H.; Hopcraft, K. I.; Jakeman, E. (2008). "Continuous and discrete stable processes". Physical Review E. 77 (1): 011109–1 to 011109–04. doi:10.1103/PhysRevE.77.011109.
8. ^ Hopcraft, K. I.; Jakeman, E.; Matthews, J. O. (2004). "Discrete scale-free distributions and associated limit theorems". Journal of Physics A: General Physics. 37 (48): L635–L642. doi:10.1088/0305-4470/37/48/L01.