Titlebook: Stochastic Differential and Difference Equations; Imre Csiszár,György Michaletzky Conference proceedings 1997 Birkhäuser Boston 1997 Mathe

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Ergodic Distributed Control for Parameter Dependent Stochastic Semilinear Systems,f a family of measures for the Markov process gives uniformly large measure to balls in the Hilbert space, then the family of measures is tight. An optimal control exists for the ergodic control problem and the optimal cost is shown to depend continuously on the system parameter.

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On the Laws of the Oseledets Spaces of Linear Stochastic Differential Equations,we shall for simplicity from the very beginning consider linear stochastic differential equations instead of linearizations for non-linear ones, and describe some smoothness results for the laws of their Oseledets spaces, using the approach of Malliavin’s calculus.

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On the Kinematics of Stochastic Mechanics,ge of variables formula. In the following section, we consider the Markov case. In Section 5, for the purpose of comparison, we give a strong form of the classical Hamilton principle. In Section 6, we present the quantum Hamilton principle.

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,The Variation of the Forecast of Lévy’s Brownian Motion as the Observation Domain Undergoes Deformad unchangeable.On the contrary, we wish to investigate the dependence of the forecast on the observation domain. In a simple case we wish to give the variation of the forecast of Lévy’s Brownian Motion (LBM) for infinitesimal deformations of the observation domain.

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Periodically Correlated Solutions to a Class of Stochastic Difference Equations,es which makes the investigation of these two classes theoretically equivalent. A survey on these questions (mainly from an algorithmic point of view) and a lot of references may be found in Boshnakov [Bo].

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