Support Theorem For Stochastic Volterra Equations
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Stochastic Differential and Difference Equations
| Author | : Imre Csiszár |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 384 |
| Release | : 1997 |
| Genre | : Mathematics |
| ISBN | : 9780817639716 |
Periodically Correlated Solutions to a Class of Stochastic Difference Equations.- On Nonlinear SDE'S whose Densities Evolve in a Finite-Dimensional Family.- Composition of Skeletons and Support Theorems.- Invariant Measure for a Wave Equation on a Riemannian Manifold.- Ergodic Distributed Control for Parameter Dependent Stochastic Semilinear Systems.- Dirichlet Forms, Caccioppoli Sets and the Skorohod Equation Masatoshi Fukushima.- Rate of Convergence of Moments of Spall's SPSA Method.- General Setting for Stochastic Processes Associated with Quantum Fields.- On a Class of Semilinear Stochastic Partial Differential Equations.- Parallel Numerical Solution of a Class of Volterra Integro-Differential Equations.- On the Laws of the Oseledets Spaces of Linear Stochastic Differential Equations.- On Stationarity of Additive Bilinear State-space Representation of Time Series.- On Convergence of Approximations of Ito-Volterra Equations.- Non-isotropic Ornstein-Uhlenbeck Process and White Noise Analysis.- Stochastic Processes with Independent Increments on a Lie Group and their Selfsimilar Properties.- Optimal Damping of Forced Oscillations Discrete-time Systems by Output Feedback.- Forecast of Lévy's Brownian Motion as the Observation Domain Undergoes Deformation.- A Maximal Inequality for the Skorohod Integral.- On the Kinematics of Stochastic Mechanics.- Stochastic Equations in Formal Mappings.- On Fisher's Information Matrix of an ARMA Process.- Statistical Analysis of Nonlinear and NonGaussian Time Series.- Bilinear Stochastic Systems with Long Range Dependence in Continuous Time.- On Support Theorems for Stochastic Nonlinear Partial Differential Equations.- Excitation and Performance in Continuous-time Stochastic Adaptive LQ-control.- Invariant Measures for Diffusion Processes in Conuclear Spaces.- Degree Theory on Wiener Space and an Application to a Class of SPDEs.- On the Interacting Measure-Valued Branching Processes.
The Stochastic Volterra Equation
| Author | : Bernt K. Øksendal |
| Publisher | : |
| Total Pages | : 29 |
| Release | : 1992 |
| Genre | : |
| ISBN | : 9788255308034 |
Stochastic Partial Differential Equations
| Author | : Helge Holden |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 238 |
| Release | : 2013-12-01 |
| Genre | : Mathematics |
| ISBN | : 1468492152 |
This book is based on research that, to a large extent, started around 1990, when a research project on fluid flow in stochastic reservoirs was initiated by a group including some of us with the support of VISTA, a research coopera tion between the Norwegian Academy of Science and Letters and Den norske stats oljeselskap A.S. (Statoil). The purpose of the project was to use stochastic partial differential equations (SPDEs) to describe the flow of fluid in a medium where some of the parameters, e.g., the permeability, were stochastic or "noisy". We soon realized that the theory of SPDEs at the time was insufficient to handle such equations. Therefore it became our aim to develop a new mathematically rigorous theory that satisfied the following conditions. 1) The theory should be physically meaningful and realistic, and the corre sponding solutions should make sense physically and should be useful in applications. 2) The theory should be general enough to handle many of the interesting SPDEs that occur in reservoir theory and related areas. 3) The theory should be strong and efficient enough to allow us to solve th,~se SPDEs explicitly, or at least provide algorithms or approximations for the solutions.
Convolution Type Stochastic Volterra Equations
| Author | : Anna Karczewska |
| Publisher | : |
| Total Pages | : 112 |
| Release | : 2007 |
| Genre | : Volterra equations |
| ISBN | : |
Stochastic Invariance and Stochastic Volterra Equations
| Author | : Eduardo Abi Jaber |
| Publisher | : |
| Total Pages | : 216 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
The present thesis deals with the theory of finite dimensional stochastic equations.In the first part, we derive necessary and sufficient geometric conditions on the coefficients of a stochastic differential equation for the existence of a constrained solution, under weak regularity on the coefficients. In the second part, we tackle existence and uniqueness problems of stochastic Volterra equations of convolution type. These equations are in general non-Markovian. We establish their correspondence with infinite dimensional equations which allows us to approximate them by finite dimensional stochastic differential equations of Markovian type. Finally, we illustrate our findings with an application to mathematical finance, namely rough volatility modeling. We design a stochastic volatility model with an appealing trade-off between flexibility and tractability.
Stochastic Differential and Difference Equations
| Author | : Imre Csiszar |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 358 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461219809 |
Stochastic Differential Equations
| Author | : Bernt Oksendal |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 218 |
| Release | : 2013-03-09 |
| Genre | : Mathematics |
| ISBN | : 3662130505 |
These notes are based on a postgraduate course I gave on stochastic differential equations at Edinburgh University in the spring 1982. No previous knowledge about the subject was assumed, but the presen tation is based on some background in measure theory. There are several reasons why one should learn more about stochastic differential equations: They have a wide range of applica tions outside mathematics, there are many fruitful connections to other mathematical disciplines and the subject has a rapidly develop ing life of its own as a fascinating research field with many interesting unanswered questions. Unfortunately most of the literature about stochastic differential equations seems to place so much emphasis on rigor and complete ness that is scares many nonexperts away. These notes are an attempt to approach the subject from the nonexpert point of view: Not knowing anything (except rumours, maybe) about a subject to start with, what would I like to know first of all? My answer would be: 1) In what situations does the subject arise? 2) What are its essential features? 3) What are the applications and the connections to other fields? I would not be so interested in the proof of the most general case, but rather in an easier proof of a special case, which may give just as much of the basic idea in the argument. And I would be willing to believe some basic results without proof (at first stage, anyway) in order to have time for some more basic applications.
