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001 ocn268966013
003 OCoLC
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006 m d
007 cr cnu---unuuu
008 081105s2001 si ob 001 0 eng d
040 _aN$T
_beng
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019 _a505144461
_a646769031
_a764502850
_a879074242
020 _a9789812810540
_q(electronic bk.)
020 _a9812810544
_q(electronic bk.)
020 _z9789810243852
020 _z9810243855
_q(alk. paper)
035 _a(OCoLC)268966013
_z(OCoLC)505144461
_z(OCoLC)646769031
_z(OCoLC)764502850
_z(OCoLC)879074242
050 4 _aTA342
_b.M35 2001eb
072 7 _aTEC
_x009000
_2bisacsh
072 7 _aTEC
_x035000
_2bisacsh
082 0 4 _a620/.001/5118
_222
049 _aMAIN
100 1 _aMamontov, Yevgeny,
_d1955-
_9434859
245 1 0 _aHigh-dimensional nonlinear diffusion stochastic processes
_h[electronic resource] :
_bmodelling for engineering applications /
_cYevgeny Mamontov, Magnus Willander.
260 _aSingapore ;
_aRiver Edge, NJ :
_bWorld Scientific,
_c2001.
300 _a1 online resource (xviii, 297 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSeries on advances in mathematics for applied sciences ;
_vv. 56
504 _aIncludes bibliographical references (and index.
588 0 _aPrint version record.
520 8 _aAnnotation 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 _aPreface; 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 _a1.5 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 _a1.12.1 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 _a2.5 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 _a3.5.1 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.
650 0 _aEngineering
_xMathematical models.
_945759
650 0 _aStochastic processes.
_91109
650 0 _aDiffusion processes.
_970248
650 0 _aDifferential equations, Nonlinear.
_95321
650 6 _aIngénierie
_xModèles mathématiques.
_9434860
650 6 _aProcessus stochastiques.
_911127
650 6 _aProcessus de diffusion.
650 6 _aÉquations différentielles non linéaires.
_911406
650 7 _aTECHNOLOGY & ENGINEERING
_xEngineering (General)
_2bisacsh
_927299
650 7 _aTECHNOLOGY & ENGINEERING
_xReference.
_2bisacsh
_927300
650 7 _aDifferential equations, Nonlinear.
_2fast
_0(OCoLC)fst00893474
_95321
650 7 _aDiffusion processes.
_2fast
_0(OCoLC)fst00893561
_970248
650 7 _aEngineering
_xMathematical models.
_2fast
_0(OCoLC)fst00910373
_945759
650 7 _aStochastic processes.
_2fast
_0(OCoLC)fst01133519
_91109
655 4 _aElectronic books.
_9396
655 0 _aElectronic books.
_9396
700 1 _aWillander, M.
_9115955
776 0 8 _iPrint version:
_aMamontov, Yevgeny, 1955-
_tHigh-dimensional nonlinear diffusion stochastic processes.
_dSingapore ; River Edge, NJ : World Scientific, 2001
_z9810243855
_z9789810243852
_w(DLC) 00053437
_w(OCoLC)45283225
830 0 _aSeries on advances in mathematics for applied sciences ;
_vv. 56.
_9386502
856 4 0 _3EBSCOhost
_uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=235902
938 _aEBL - Ebook Library
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938 _aebrary
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938 _aEBSCOhost
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938 _aYBP Library Services
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