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.
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, .
In probability theory and related fields, a stochastic (stəˈkæstɪk) or random process is a mathematical object usually defined as a sequence of random variables, where the index of the sequence has the interpretation of time. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. Examples include the growth of a bacterial population, an electrical current fluctuating due to thermal noise, or the movement of a gas molecule.
Covers the properties and construction of Poisson processes from i.i.d. Exp(X) random variables, emphasizing the importance of the process rate and jump time distributions.
Consider a stream of status updates generated by a source, where each update is of one of two types: high priority or ordinary (low priority). These updates are to be transmitted through a network to a monitor. However, the transmission policy of each pack ...
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Springer2019
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Consider a stream of status updates generated by a source, where each update is of one of two types: priority or ordinary; these updates are to be transmitted through a network to a monitor. We analyze a transmission policy that treats updates depending on ...