Results 151 to 160 of about 1,219,628 (202)

Wiener distributions and white noise analysis

Applied Mathematics & Optimization, 1992
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Betounes, David E., Redfern, Mylan
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White-Noise Analysis of Biological Systems

Journal of Medical Engineering & Technology, 1978
The linear and nonlinear operations of a biological system can be represented by a set of functions called Wiener kernels. This type of analysis is becoming increasingly important in the field of biological systems analysis. This paper reviews the theoretical and practical aspects of testing a biological system with white-noise and provides a guide for
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White Noise and Stochastic Analysis

1990
In this article we describe the fundamental notions of T. Hida’s white noise calculus and show how they can be used for an approach of stochastic analysis with a more functional-analytic flavour. In particular, we shall review the construction of stochastic integrals (more general than in Ito’s calculus) of Kuo and Russek [KR 88] and show that Ito’s ...
J. Asch, J. Potthoff
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Spectral approach to white noise analysis

Ukrainian Mathematical Journal, 1994
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Berezanskij, Yu. M., Livinskij, V. O., Litvinov, E. M.   +2 more
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On Differential Operators in White Noise Analysis

Acta Applicandae Mathematica, 2000
\textit{T. Deck}, \textit{G. Våge} and the author [ibid. 48, No. 1, 91-112 (1997; Zbl 0892.60050)] formulated T. Hida's white noise analysis on a general probability space. However, the problem of showing that the differential operators defined in the paper quoted above are well-defined was left open.
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Renormalizations in White Noise Analysis

2011
Renormalization has been applied in many places by using a method fitting for each situation. In this report, we are in a position where a white noise \(\{ \dot B(t), t \in R^1 \}\) is taken to be a variable system of random functions \(\varphi (\dot B).\) With this setting, renormalization plays the role that lets \(\varphi (\dot B)\) become a ...
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White Noise Analysis and Chaos Expansions

2017
In the framework of white noise analysis, random variables and stochastic processes can be represented in terms of Fourier series in a Hilbert space orthogonal basis, namely in their chaos expansion forms. We briefly summarize basic concepts and notations of white noise analysis, characterize different classes of stochastic processes (test, square ...
Tijana Levajković, Hermann Mena
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White Noise Analysis and Applications

1994
This article presents some of the recent development in white noise analysis as an infinite dimensional calculus.
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FEYNMAN INTEGRALS AND WHITE NOISE ANALYSIS

Stochastic Analysis and Mathematical Physics, 2004
We review some basic notions and results of white noise analysis that are used in the construction of the Feynman integrand as a generalized White Noise functional for different classes of interactions. After sketching this construction for a large class of potentials we show that the resulting Feynman integrals solve the Schrdinger equation.
JOSÉ L. SILVA, LUDWIG STREIT
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Stochastic integration via white noise analysis

Nonlinear Analysis: Theory, Methods & Applications, 1997
Using the set up of white noise analysis, the multiplication operator \(\dot B(t)\cdot\) is written as \(\partial^*_t+\partial_t\), where \(\partial^*_t\) and \(\partial_t\) are the creation and annihilation operator at the time \(t\). Because of the fact that for non-anticipation function \(\phi\), \(\partial_t\phi(t)=0\), the Itô integral of non ...
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