đźš§ Clarify what it means for discrete- vs continuous-valued RVs.

đźš§ all of these are about sums; but presumably we can construct this over other algebraic structures of distributions, e.g. max-stable processes.

For now, some handy definition disambiguation.

## Infinitely divisible

The LĂ©vy process quality.

A probability distribution is infinitely divisible if it can be expressed as the probability distribution of the sum of any arbitrary natural number of independent and identically distributed random variables. i.e.Â The distribution \(F\) is *infintely divisible* if, for every positive integer \(n\), there exist \(n\) i.i.d. RVs whose sum

\[X_1 + \dots + X_n = S_n \sim F\]

## Decomposable

The distribution of \(X\) is *decomposable* if there are 2 or more non-constant RVs, not necessarily in the same family, whose sum has this distribution. Not a very strong property, but the cases where an RV fails to possess even this are curious. đźš§

## Self-decomposable

Decomposable, but the components must be in the same family.

## Stable

đźš§ This ection is abstruse nonsense tha I need to fix some day. Do not read it right now as it makes no sense.

A distribution or a random variable is said to be *stable* if a linear combination of two independent copies of a random sample has the same distribution, up to location and scale parameters.

This induces at least 2 families of infinitely divisible distributions, the discrete and continuous stable family. See (van Harn and Steutel 1993).

A well-known distribution construction: the stable distribution class.

For continuous-valued continuous/discrete indexed stochastic process, \(X(t)\), \(\alpha\)-*stability* implies that the marginal law of the value of the process at certain times satisfies a stability equation

\[X(a) \simeq W^{1/\alpha}X(b),\] for \(0 < a < b\), \(\alpha> 0\) and \(W\sim \operatorname{Unif}([0,1])\perp X\).

The marginal distributions of such processes are those of the \(\alpha\)-stable processes. For \(\alpha=2\) we have Gaussians and for \(\alpha=1\), the Cauchy law.

# Refs

Cahoy, Dexter O., Vladimir V. Uchaikin, and Wojbor A. Woyczynski. 2010. â€śParameter Estimation for Fractional Poisson Processes.â€ť *Journal of Statistical Planning and Inference* 140 (11): 3106â€“20. https://doi.org/10.1016/j.jspi.2010.04.016.

Harn, K. van, and F. W. Steutel. 1993. â€śStability Equations for Processes with Stationary Independent Increments Using Branching Processes and Poisson Mixtures.â€ť *Stochastic Processes and Their Applications* 45 (2): 209â€“30. https://doi.org/10.1016/0304-4149(93)90070-K.

Harn, K. van, F. W. Steutel, and W. Vervaat. 1982. â€śSelf-Decomposable Discrete Distributions and Branching Processes.â€ť *Zeitschrift FĂĽr Wahrscheinlichkeitstheorie Und Verwandte Gebiete* 61 (1): 97â€“118. https://doi.org/10.1007/BF00537228.

HoudrĂ©, Christian. 2002. â€śRemarks on Deviation Inequalities for Functions of Infinitely Divisible Random Vectors.â€ť *The Annals of Probability* 30 (3): 1223â€“37. https://doi.org/10.1214/aop/1029867126.

Janson, Svante. 2011. â€śStable Distributions,â€ť December. http://arxiv.org/abs/1112.0220.

Steutel, F. W., and K. van Harn. 1979. â€śDiscrete Analogues of Self-Decomposability and Stability.â€ť *The Annals of Probability* 7 (5): 893â€“99. https://doi.org/10.1214/aop/1176994950.