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 (i.i.d.) random variables. The characteristic function of any infinitely divisible distribution is then called an infinitely divisible characteristic function.
More rigorously, the probability distribution F is infinitely divisible if, for every positive integer n, there exist n i.i.d. random variables Xn1, ..., Xnn whose sum Sn = Xn1 + ... + Xnn has the same 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.
Examples of continuous distributions that are infinitely divisible are the normal distribution, the Cauchy distribution, the Lévy distribution, and all other members of the stable distribution family, as well as the Gamma distribution, the chi-square distribution, the Wald distribution, the Log-normal distribution and the Student's t-distribution.
Among the discrete distributions, examples are the Poisson distribution and the negative binomial distribution (and hence the geometric distribution also). The one-point distribution whose only possible outcome is 0 is also (trivially) infinitely divisible.
The uniform distribution and the binomial distribution are not infinitely divisible, nor are any other distributions with bounded support (≈ finite-sized domain), other than the one-point distribution mentioned above. The distribution of the reciprocal of a random variable having a Student's t-distribution is also not infinitely divisible.
Any compound Poisson distribution is infinitely divisible; this follows immediately from the definition.
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
En théorie des probabilités et en statistiques, la loi de Poisson est une loi de probabilité discrète qui décrit le comportement du nombre d'événements se produisant dans un intervalle de temps fixé, si ces événements se produisent avec une fréquence moyenne ou espérance connue, et indépendamment du temps écoulé depuis l'événement précédent. gauche|vignette|Chewing gums sur un trottoir. Le nombre de chewing gums sur un pavé est approximativement distribué selon une loi de Poisson.
En mathématiques et plus particulièrement en théorie des probabilités et en statistique, la fonction caractéristique d'une variable aléatoire réelle est une quantité qui détermine de façon unique sa loi de probabilité. Si cette variable aléatoire a une densité, alors la fonction caractéristique est la transformée de Fourier inverse de la densité. Les valeurs en zéro des dérivées successives de la fonction caractéristique permettent de calculer les moments de la variable aléatoire.
In probability theory, an indecomposable distribution is a probability distribution that cannot be represented as the distribution of the sum of two or more non-constant independent random variables: Z ≠ X + Y. If it can be so expressed, it is decomposable: Z = X + Y. If, further, it can be expressed as the distribution of the sum of two or more independent identically distributed random variables, then it is divisible: Z = X1 + X2. The simplest examples are Bernoulli-distributeds: if then the probability distribution of X is indecomposable.
We cover the theory and applications of sparse stochastic processes (SSP). SSP are solutions of differential equations driven by non-Gaussian innovations. They admit a parsimonious representation in a
This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python sou
Étudier les concepts fondamentaux d'analyse et le calcul différentiel et intégral des fonctions réelles d'une variable.
Couvre la théorie du mouvement brownien, de la diffusion et des promenades aléatoires, en mettant l'accent sur la théorie d'Einstein pour le mouvement unidimensionnel.
We establish a Chung-type law of the iterated logarithm and the exact local and uniform moduli of continuity for a large class of anisotropic Gaussian random fields with a harmonizable-type integral representation and the property of strong local nondeterm ...
In the field of plasma physics, suprathermal ions are encountered e.g. in astrophysical jets, the solar wind, as well as fusion plasmas, where they originate from neutral beam injection or fusion reactions. One aspect of shared interest with astrophysical ...
EPFL2020
Given a sequence L & x2d9;epsilon of Levy noises, we derive necessary and sufficient conditions in terms of their variances sigma 2(epsilon) such that the solution to the stochastic heat equation with noise sigma(epsilon)-1L & x2d9;epsilon converges in law ...