Wiener process

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Wiener process

[′vē·nər ‚prä·səs]
(mathematics)
A stochastic process with normal density at each stage, arising from the study of Brownian motion, which represents the limit of a sequence of experiments. Also known as Gaussian noise.
References in periodicals archive ?
Yoo, "Change of scale formulas for a generalized conditional Wiener integral," Bulletin of the Korean Mathematical Society, vol.
We now have the following relationships among the conditional Fourier-Feynman transform, the conditional convolution products, and the generalized Wiener integrals of the functions in [mathematical expression not reproducible].
Cho, "Analogues of conditional Wiener integrals with drift and initial distribution on a function space," Abstract and Applied Analysis, vol.
and is called the Wiener integral with respect to [W.sup.H].
Valkeila, "Inequalities for the moments of Wiener integrals with respect to a fractional Brownian motion," Statistics & Probability Letters, vol.
Here in this approximation we have the double Wiener integrals [A.sub.11], [A.sub.12], [A.sub.21], and [A.sub.22].
It describes preliminary results on covariance and associated RKHS, the Gaussian process, the definition of multiple Wiener integrals for a general Gaussian process and stochastic integration for Gaussian random fields, Skorokhod and Malliavin derivatives for Gaussian random fields, filtering with general Gaussian noise, equivalence and singularity, and the Markov property of Gaussian fields and Dirichlet forms.