Wiener measure(2)
The probability distribution of a Brownian motion , , , where is a probability space. The Wiener measure is denoted by or . Brownian motion is a Gaussian process such that
Given a Brownian motion , one can form a new Brownian motion satisfying:
i) is continuous in for almost all .
ii) for every .
Such a process is called a continuous version of .
The Kolmogorov–Prokhorov theorem tells that the probability distribution of the Brownian motion can be introduced in the space of all continuous functions on .
Let be the topological Borel field (cf. also Borel field of sets) of subsets of . The measure space thus obtained is the Wiener measure space.
The integral of a -measurable functional on with respect to is defined in the usual manner. (See also Stochastic integral.)
An elementary and important example of a -measurable functional of is a stochastic bilinear form, given by , where is an -function. It is usually denoted by . It is, in fact, defined by for smooth functions . For a general , can be approximated by stochastic bilinear forms defined by smooth functions . An integral of this type is called a Wiener integral. Under certain restrictions, such as non-anticipation, the integral can be extended to the case where the integrand is a functional of and . And an even more general case has been proposed.
The class of general (non-linear) functionals of is introduced as follows. Let be the Hilbert space of all complex-valued, square--integrable functionals on . Then, admits a direct sum decomposition (Fock space)
The subspace is spanned by the Fourier–Hermite polynomials of degree , which are of the form
where and is a complete orthonormal system in the Hilbert space . The space can be interpreted as the space of multiple Wiener integrals of degree , due to K. Itô.
References
[a1] | R. Cameron, W.T. Martin, "The orthogonal development of non-linear functionals in series of Fourier–Hermite functionals" Ann. of Math. (2) , 48 pp. 385–392 |
[a2] | T. Hida, "Brownian motion" , Applications of Mathematics , 11 , Springer (1980) |
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