A generalization of the notion of Wiener process for the case of multivariate time. This generalization can be performed in two ways.
-parameter Wiener field (Brownian motion).
Let be a Gaussian separable real-valued field on with zero mean and covariance function
where . Such a field can be regarded as the distribution function of a white noise on , which is a random function on bounded Borel sets in such that has a normal distribution with zero mean and covariance function [a11]. Here, denotes the Lebesgue measure on . The following equality holds: , where is a parallelepiped in .
The random field was introduced by T. Kitagava [a14] in connection with its applications to statistical problems. N.N. Chentsov proved the almost sure continuity of the sample functions of [a3]. For any fixed time variables, is a one-parameter Wiener process as a function of the free time variable. Some properties of are similar to the corresponding properties of the Wiener process: the sample functions of almost surely satisfy Hölder's stochastic condition with exponent [a2]; various forms of the law of the iterated logarithm hold true ([a5], [a18], [a20]). An exact formula for exists only for the Wiener process. For , only lower and upper bounds (, [a12]) and some asymptotic formulas for [a21] have been derived so far (1998). The level sets of have an extremely complicated geometric and topological structure ([a8], [a9], [a10], [a13]). R.J. Adler [a1] showed that the Hausdorff dimension of these sets equals .
The Wiener process is a Markov process: conditional of the present value , the past () and the future () are independent. For the multivariate case there are several definitions of the Markovian property. Let denote a family of Jordan surfaces in . Each such surface divides into two parts: , the interior of , or the "past" , and , the exterior of , or the "future" . A random field is said to be Markovian with respect to the family if for arbitrary from and arbitrary , , the random variables and are conditionally independent given [a24]. A Wiener field is a Markovian field with respect to the family consisting of all finite unions of rectangles whose sides are parallel to the coordinate axes ([a22], [a23]). For , its sharp field and germ field are defined, respectively, by and , where is an -neighbourhood of . A Wiener sheet is germ Markovian, i.e. for every bounded subset , the fields and are conditionally independent given ([a6], [a7], [a22]).
Among the objects closely related to are the Wiener pillow and the Wiener bridge. These are Gaussian random fields (cf. also Random field) on with zero mean and covariance functions
Lévy -parameter Brownian motion.
This is a Gaussian random field on with zero mean and covariance function
where [a15]. When , becomes a Wiener process. The random variables clearly form a Wiener process if moves along some semi-straight line with terminal point . has the following representation in terms of white noise:
where is the surface area of the -dimensional unit sphere [a17]. H.P. McKean Jr. [a16] has shown that is germ Markovian with respect to closed bounded subsets in for each odd , whereas for each even the Markovian property does not hold.
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Wiener field. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Wiener_field&oldid=24596