In probability theory, a probability distribution is infinitely divisible if it can be expressed as the probability distribution of the sum of an arbitrary number of independent and identically distributed random variables. The characteristic function of any infinitely divisible distribution is then called an infinitely divisible characteristic function.^[1]

More rigorously, the probability distribution F is infinitely divisible if, for every positive integer n, there exist n independent identically distributed random variables X**n1, ..., X**nn whose sum S**n = X**n1 + … + X**nn has the distribution F.

The concept of infinite divisibility of probability distributions was introduced in 1929 by Bruno de Finetti. This type of decomposition of a distribution is used in probability and statistics to find families of probability distributions that might be natural choices for certain models or applications. Infinitely divisible distributions play an important role in probability theory in the context of limit theorems.^[1]

The Poisson distribution, the negative binomial distribution, the Gamma distribution and the degenerate distribution are examples of infinitely divisible distributions; as are the normal distribution, Cauchy distribution and all other members of the stable distribution family. The uniform distribution and the binomial distribution are not infinitely divisible, nor are any other distributions with bounded (finite) support.^[2] The Student's t-distribution is infinitely divisible, while the distribution of the reciprocal of a random variable having a Student's t-distribution, is not.^[3]

All the compound Poisson distributions are infinitely divisible.

Infinitely divisible distributions appear in a broad generalization of the central limit theorem: the limit as n → +∞ of the sum S**n = X**n1 + … + X**nn of independent uniformly asymptotically negligible (u.a.n.) random variables within a triangular array

approaches — in the weak sense — an infinitely divisible distribution. The uniformly asymptotically negligible (u.a.n.) condition is given by

Thus, for example, if the uniform asymptotic negligibility (u.a.n.) condition is satisfied via an appropriate scaling of identically distributed random variables with finite variance, the weak convergence is to the normal distribution in the classical version of the central limit theorem. More generally, if the u.a.n. condition is satisfied via a scaling of identically distributed random variables (with not necessarily finite second moment), then the weak convergence is to a stable distribution. On the other hand, for a triangular array of independent (unscaled) Bernoulli random variables where the u.a.n. condition is satisfied through

the weak convergence of the sum is to the Poisson distribution with mean λ as shown by the familiar proof of the law of small numbers.

Every infinitely divisible probability distribution corresponds in a natural way to a Lévy process. A Lévy process is a stochastic process { Lt : t ≥ 0 } with stationary independent increments, where stationary means that for s < t, the probability distribution of L**t − L**s depends only on t − s and where independent increments means that that difference L**t − L**s is independent of the corresponding difference on any interval not overlapping with [s, t], and similarly for any finite number of mutually non-overlapping intervals.

If { Lt : t ≥ 0 } is a Lévy process then, for any t ≥ 0, the random variable L**t will be infinitely divisible: for any n, we can choose (X**n0, X**n1, …, X**nn) = (L**t/n − L0, L2t/n − L**t/n, …, L**t − L(n−1)t/n). Similarly, L**t − L**s is infinitely divisible for any s < t.

On the other hand, if F is an infinitely divisible distribution, we can construct a Lévy process { Lt : t ≥ 0 } from it. For any interval [s, t] where t − s > 0 equals a rational number p/q, we can define L**t − L**s to have the same distribution as X**q1 + X**q2 + … + X**qp. Irrational values of t − s > 0 are handled via a continuity argument.