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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In probability theory, a Lévy process, named after the French mathematician Paul Lévy, is a stochastic process with independent, stationary increments: it represents the motion of a point whose successive displacements are random, in which displacements in pairwise disjoint time intervals are independent, and displacements in different time intervals of the same length have identical probability distributions. A Lévy process may thus be viewed as the continuous-time analog of a random walk.
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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 .