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Titlebook: Recent Developments in Infinite-Dimensional Analysis and Quantum Probability; Papers in Honour of Luigi Accardi,Hui-Hsiung Kuo,Ludwig Stre

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樓主: 爆裂
51#
發(fā)表于 2025-3-30 09:58:11 | 只看該作者
52#
發(fā)表于 2025-3-30 12:26:17 | 只看該作者
Generalized Functions in Signal Theory,smission function cannot be defined. Starting from the requirements that have to be taken for a function space, if it should be suitable for a signal theory, generalized functions are introduced. Moreover, the connections between such a signal theory and the theory of white noise are discussed.
53#
發(fā)表于 2025-3-30 16:56:59 | 只看該作者
54#
發(fā)表于 2025-3-30 22:14:28 | 只看該作者
Quantum Mechanics and Brownian Motions,ns of the quantum fluctuation within the framework of new quantization schemes, such as . and/or . quantizations, for increasing additional . time other than the ordinary one. On the same basis we also show that a D-dimensional quantum system is equivalent to a (D + 1)-dimensional classical system.
55#
發(fā)表于 2025-3-31 04:19:10 | 只看該作者
56#
發(fā)表于 2025-3-31 06:47:16 | 只看該作者
57#
發(fā)表于 2025-3-31 13:15:51 | 只看該作者
Exponential Moments of Solutions for Nonlinear Equations with Catalytic Noise and Large Deviation,Nonlinear equation with catalytic noise is considered. We discuss the existence of catalytic superprocess associated with the equation and derive the exponential moment formula. Moreover, we prove the large deviation principle for catalytic superprocesses.
58#
發(fā)表于 2025-3-31 15:39:53 | 只看該作者
Ornstein-Uhlenbeck Path Integral and Its Application,Introducing a path integral for the Ornstein-Uhlenbeck process distorted by a potential .(.), we find out the . → ∞ limit of the probability distributions of .[.] := 1/.. ∫..(.(.)) d. for Ornstein-Uhlenbeck process .(.), with appropriate values of the exponent . that depend on .. The results are compared with those for the Wiener process.
59#
發(fā)表于 2025-3-31 20:24:44 | 只看該作者
60#
發(fā)表于 2025-3-31 22:35:07 | 只看該作者
A White Noise Approach to Stochastic Neumann Boundary-Value Problems,We illustrate the use of white noise analysis in the solution of stochastic partial differential equations by explicitly solving the stochastic Neumann boundary-value problem .where . is a uniformly elliptic linear partial differential operator and .(.), . ∈ ?., is .-parameter white noise.
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