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High-dimensional nonlinear diffusion stochastic processes : modelling for engineering applications /

Annotation This book is one of the first few devoted to high-dimensional diffusion stochastic processes with nonlinear coefficients. These processes are closely associated with large systems of Ito's stochastic differential equations and with discretized-in-the-parameter versions of Ito's...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mamontov, Yevgeny, 1955-
Otros Autores: Willander, M.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; River Edge, NJ : World Scientific, 2001.
Colección:Series on advances in mathematics for applied sciences ; v. 56.
Temas:
Acceso en línea:Texto completo

MARC

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100 1 |a Mamontov, Yevgeny,  |d 1955-  |1 https://id.oclc.org/worldcat/entity/E39PCjDbTbpbbBgGWPTJkHXwyb 
245 1 0 |a High-dimensional nonlinear diffusion stochastic processes :  |b modelling for engineering applications /  |c Yevgeny Mamontov, Magnus Willander. 
260 |a Singapore ;  |a River Edge, NJ :  |b World Scientific,  |c 2001. 
300 |a 1 online resource (xviii, 297 pages) 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Series on advances in mathematics for applied sciences ;  |v v. 56 
504 |a Includes bibliographical references (and index. 
588 0 |a Print version record. 
520 8 |a Annotation This book is one of the first few devoted to high-dimensional diffusion stochastic processes with nonlinear coefficients. These processes are closely associated with large systems of Ito's stochastic differential equations and with discretized-in-the-parameter versions of Ito's stochastic differential equations that are nonlocally dependent on the parameter. The latter models include Ito's stochastic integro-differential, partial differential and partial integro-differential equations. The book presents the new analytical treatment which can serve as the basis of a combined, analytical -- numerical approach to greater computational efficiency. Some examples of the modelling of noise in semiconductor devices are provided. 
505 0 |a Preface; Contents; Chapter 1 Introductory Chapter; 1.1 Prerequisites for Reading; 1.2 Random Variable. Stochastic Process. Random Field. High-Dimensional Process. One-Point Process; 1.3 Two-Point Process. Expectation. Markov Process. Example of Non-Markov Process Associated with Multidimensional Markov Process; 1.4 Preceding Subsequent and Transition Probability Densities. The Chapman-Kolmogorov Equation. Initial Condition for Markov Process; 1.4.1 The Chapman-Kolmogorov equation; 1.4.2 Initial condition for Markov process. 
505 8 |a 1.5 Homogeneous Markov Process. Example of Markov Process: The Wiener Process1.6 Expectation Variance and Standard Deviations of Markov Process; 1.7 Invariant and Stationary Markov Processes. Covariance. Spectral Densities; 1.8 Diffusion Process; 1.9 Example of Diffusion Processes: Solutions of Ito's Stochastic Ordinary Differential Equation; 1.10 The Kolmogorov Backward Equation; 1.11 Figures of Merit. Diffusion Modelling of High-Dimensional Systems; 1.12 Common Analytical Techniques to Determine Probability Densities of Diffusion Processes. The Kolmogorov Forward Equation. 
505 8 |a 1.12.1 Probability density1.12.2 Invariant probability density; 1.12.3 Stationary probability density; 1.13 The Purpose and Content of This Book; Chapter 2 Diffusion Processes; 2.1 Introduction; 2.2 Time-Derivatives of Expectation and Variance; 2.3 Ordinary Differential Equation Systems for Expectation; 2.3.1 The first-order system; 2.3.2 The second-order system; 2.3.3 Systems of the higher orders; 2.4 Models for Noise-Induced Phenomena in Expectation; 2.4.1 The case of stochastic resonance; 2.4.2 Practically efficient implementation of the second-order system. 
505 8 |a 2.5 Ordinary Differential Equation System for Variance2.5.1 Damping matrix; 2.5.2 The uncorrelated-matrixes approximation; 2.5.3 Nonlinearity of the drift function; 2.5.4 Fundamental limitation of the state-space-independent approximations for the diffusion and damping matrixes; 2.6 The Steady-State Approximation for The Probability Density; Chapter 3 Invariant Diffusion Processes; 3.1 Introduction; 3.2 Preliminary Remarks; 3.3 Expectation. The Finite-Equation Method; 3.4 Explicit Expression for Variance; 3.5 The Simplified Detailed-Balance Approximation for Invariant Probability Density. 
505 8 |a 3.5.1 Partial differential equation for logarithm of the density3.5.2 Truncated equation for the logarithm and the detailed-balance equation; 3.5.3 Case of the detailed balance; 3.5.4 The detailed-balance approximation; 3.5.5 The simplified detailed-balance approximation. Theorem on the approximating density; 3.6 Analytical-Numerical Approach to Non-Invariant and Invariant Diffusion Processes; 3.6.1 Choice of the bounded domain of the integration; 3.6.2 Evaluation of the multifold integrals. The Monte Carlo technique; 3.6.3 Summary of the approach; 3.7 Discussion. 
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650 0 |a Engineering  |x Mathematical models. 
650 0 |a Stochastic processes. 
650 0 |a Diffusion processes. 
650 0 |a Differential equations, Nonlinear. 
650 2 |a Stochastic Processes 
650 6 |a Ingénierie  |x Modèles mathématiques. 
650 6 |a Processus stochastiques. 
650 6 |a Processus de diffusion. 
650 6 |a Équations différentielles non linéaires. 
650 7 |a TECHNOLOGY & ENGINEERING  |x Engineering (General)  |2 bisacsh 
650 7 |a TECHNOLOGY & ENGINEERING  |x Reference.  |2 bisacsh 
650 7 |a Differential equations, Nonlinear  |2 fast 
650 7 |a Diffusion processes  |2 fast 
650 7 |a Engineering  |x Mathematical models  |2 fast 
650 7 |a Stochastic processes  |2 fast 
700 1 |a Willander, M. 
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830 0 |a Series on advances in mathematics for applied sciences ;  |v v. 56. 
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