View source for Stochastic process with independent increments

A [[Stochastic process|stochastic process]] <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902401.png" /> such that for any natural number <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902402.png" /> and all real <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902403.png" />, the increments

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are mutually-independent random variables. A stochastic process with independent increments is called homogeneous if the probability distribution of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902405.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902406.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902407.png" />, depends only on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902408.png" /> and not on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s0902409.png" />. Since the result of adding any non-random function <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024010.png" /> to <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024011.png" /> is again a stochastic process with independent increments, the realizations of such processes can be arbitrarily irregular. However, by suitably  "centering"  the process (say by subtracting from <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024012.png" /> the function <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024013.png" /> defined by the relation <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024014.png" />), one can make more definite judgements about the structure of the  "centred"  process. There are at most countably-many (non-random) points <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024015.png" /> at which <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024016.png" /> has random jumps

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and the difference

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is a stochastically-continuous stochastic process with independent increments: for any <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024019.png" /> and <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024020.png" />,

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A [[Wiener process|Wiener process]] and a [[Poisson process|Poisson process]] are examples of stochastically-continuous stochastic processes with independent increments (and realizations of the first are continuous with probability one, while realizations of the second are step functions with jumps equal to one). An important example of a stochastic process with independent increments is that of a stable process (cf. [[Stable distribution|Stable distribution]]). Realizations of a stochastically-continuous stochastic process with independent increments can only have discontinuities of the first kind, with probability one. The distribution of the values of such a process for any <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090240/s09024022.png" /> is infinitely divisible (see [[Infinitely-divisible distribution|Infinitely-divisible distribution]]). In studying stochastic processes with independent increments one can apply the method of characteristic functions (cf. [[Characteristic function|Characteristic function]]). Problems on the probability of a process crossing a boundary and on the probability distribution of the first crossing time are solved using the so-called [[Factorization identities|factorization identities]].

====References====
<table><TR><TD valign="top">[1]</TD> <TD valign="top">  I.I. [I.I. Gikhman] Gihman,   A.V. [A.V. Skorokhod] Skorohod,   "The theory of stochastic processes" , '''2''' , Springer  (1975)  (Translated from Russian)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top">  A.V. [A.V. Skorokhod] Skorohod,   "Random processes with independent increments" , Kluwer  (1991)  (Translated from Russian)</TD></TR></table>



====Comments====
For additional references see [[Stochastic process|Stochastic process]].