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Stochastic process, generalized

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A stochastic process depending on a continuous (time) argument t and such that its values at fixed moments of time do not, in general, exist, but the process has only "smoothed values" X ( \phi ) describing the results of measuring its values by means of all possible linear measuring devices with sufficiently smooth weight function (or impulse transition function) \phi ( t) . A generalized stochastic process x ( \phi ) is a continuous linear mapping of the space D of infinitely-differentiable functions \phi of compact support (or any other space of test functions used in the theory of generalized functions) into the space L _ {0} of random variables X defined on some probability space. Its realizations x ( \phi ) are ordinary generalized functions of the argument t . Ordinary stochastic processes X ( t) can also be regarded as generalized stochastic processes, for which

X ( \phi ) = \int\limits _ {- \infty } ^ \infty \phi ( t) X ( t) d t ;

this is particularly useful in combination with the fact that a generalized stochastic process X always has derivatives X ^ {(n)} of any order n , given by

X ^ {(n)} ( \phi ) = ( - 1 ) ^ {n} X ( \phi ^ {(n)} )

(see, for example, Stochastic process with stationary increments). The most important example of a generalized stochastic process of non-classical type is that of white noise. A generalization of the concept of a generalized stochastic process is that of a generalized random field.

For references, see Random field, generalized.

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References

[a1] I.M. Gel'fand, N.Ya. Vilenkin, "Generalized functions. Applications of harmonic analysis" , 4 , Acad. Press (1964) (Translated from Russian)
How to Cite This Entry:
Stochastic process, generalized. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Stochastic_process,_generalized&oldid=51778
This article was adapted from an original article by A.M. Yaglom (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article