In probability theory, independent increments are a property of stochastic processes and random measures. Most of the time, a process or random measure has independent increments by definition, which underlines their importance. Some of the stochastic processes that by definition possess independent increments are the Wiener process, all Lévy processes, all additive process
and the Poisson point process.
Let be a stochastic process. In most cases, or . Then the stochastic process has independent increments if and only if for every and any choice with
the random variables
are stochastically independent.
A random measure has got independent increments if and only if the random variables are stochastically independent for every selection of pairwise disjoint measurable sets and every .
Let be a random measure on and define for every bounded measurable set the random measure on as
Then is called a random measure with independent S-increments, if for all bounded sets and all the random measures are independent.
Independent increments are a basic property of many stochastic processes and are often incorporated in their definition.
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En théorie des probabilités, un processus de Lévy, nommé d'après le mathématicien français Paul Lévy, est un processus stochastique en temps continu, continu à droite limité à gauche (càdlàg), partant de 0, dont les accroissements sont stationnaires et indépendants (cette notion est expliquée ci-dessous). Les exemples les plus connus sont le processus de Wiener et le processus de Poisson.
In probability theory, independent increments are a property of stochastic processes and random measures. Most of the time, a process or random measure has independent increments by definition, which underlines their importance. Some of the stochastic processes that by definition possess independent increments are the Wiener process, all Lévy processes, all additive process and the Poisson point process. Let be a stochastic process. In most cases, or .
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, .
Couvre les équations différentielles stochastiques, l'accroissement Wiener, le lemma d'Ito, et l'intégration du bruit blanc dans la modélisation financière.