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|a Mamontov, Yevgeny,
|d 1955-
|1 https://id.oclc.org/worldcat/entity/E39PCjDbTbpbbBgGWPTJkHXwyb
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|a High-dimensional nonlinear diffusion stochastic processes :
|b modelling for engineering applications /
|c Yevgeny Mamontov, Magnus Willander.
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260 |
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|a Singapore ;
|a River Edge, NJ :
|b World Scientific,
|c 2001.
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300 |
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|a 1 online resource (xviii, 297 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a data file
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|a Series on advances in mathematics for applied sciences ;
|v v. 56
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|a Includes bibliographical references (and index.
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|a Print version record.
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|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.
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|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.
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|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.
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|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.
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|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.
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|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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|a eBooks on EBSCOhost
|b EBSCO eBook Subscription Academic Collection - Worldwide
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590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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650 |
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|a Engineering
|x Mathematical models.
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650 |
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|a Stochastic processes.
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650 |
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|a Diffusion processes.
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650 |
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|a Differential equations, Nonlinear.
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2 |
|a Stochastic Processes
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|a Ingénierie
|x Modèles mathématiques.
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650 |
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|a Processus stochastiques.
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|a Processus de diffusion.
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|a Équations différentielles non linéaires.
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|a TECHNOLOGY & ENGINEERING
|x Engineering (General)
|2 bisacsh
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|a TECHNOLOGY & ENGINEERING
|x Reference.
|2 bisacsh
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|
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|a Differential equations, Nonlinear
|2 fast
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|
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|a Diffusion processes
|2 fast
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|
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|a Engineering
|x Mathematical models
|2 fast
|
650 |
|
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|a Stochastic processes
|2 fast
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1 |
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|a Willander, M.
|
758 |
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|i has work:
|a High-dimensional nonlinear diffusion stochastic processes (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCH6dmX34dPQRRkrJdyGKBd
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
776 |
0 |
8 |
|i Print version:
|a Mamontov, Yevgeny, 1955-
|t High-dimensional nonlinear diffusion stochastic processes.
|d Singapore ; River Edge, NJ : World Scientific, 2001
|z 9810243855
|z 9789810243852
|w (DLC) 00053437
|w (OCoLC)45283225
|
830 |
|
0 |
|a Series on advances in mathematics for applied sciences ;
|v v. 56.
|
856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1679503
|z Texto completo
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