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Titlebook: Analysis and Approximation of Rare Events; Representations and Amarjit Budhiraja,Paul Dupuis Book 2019 Springer Science+Business Media, LL

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發(fā)表于 2025-3-21 19:54:29 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Analysis and Approximation of Rare Events
期刊簡稱Representations and
影響因子2023Amarjit Budhiraja,Paul Dupuis
視頻videohttp://file.papertrans.cn/157/156153/156153.mp4
發(fā)行地址Illustrates the use of these methods using a wide variety of discrete and continuous time models.Timely and important topic with significant developments over the last 15 years.Includes both theory an
學(xué)科分類Probability Theory and Stochastic Modelling
圖書封面Titlebook: Analysis and Approximation of Rare Events; Representations and  Amarjit Budhiraja,Paul Dupuis Book 2019 Springer Science+Business Media, LL
影響因子.This book presents broadly applicable methods for the large deviation and moderate deviation analysis of discrete and continuous time stochastic systems. A feature of the book is the systematic use of variational representations for quantities of interest such as normalized logarithms of probabilities and expected values.? By characterizing a large deviation principle in terms of Laplace asymptotics, one converts the proof of large deviation limits into the convergence of variational representations. These features are illustrated though their application to a broad range of discrete and continuous time models, including stochastic partial differential equations, processes with discontinuous statistics, occupancy models, and many others. The tools used in the large deviation analysis also turn out to be useful in understanding Monte Carlo schemes for the numerical approximation of the same probabilities and expected values. This connection is illustrated through the design and analysis of importance sampling and splitting schemes for rare event estimation. ?The book assumes a solid background in weak convergence of probability measures and stochastic analysis, and is suitable for
Pindex Book 2019
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Digital Service Delivery in Africaax some of the assumptions made in these works. Results on moderate deviation principles in this chapter are based on the recent work [41]. We do not aim for maximal generality, and from the proofs it is clear that many other models (e.g., time inhomogeneous jump diffusions, SDEs with delay) can be treated in an analogous fashion.
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Abstract Sufficient Conditions for Large and Moderate Deviations in the Small Noise Limitultiplicative noise, namely settings in which the noise term is multiplied by a state-dependent coefficient). For these systems, one can view the mapping that takes the noise into the state of the system as “nearly” continuous, and it is this property that allows a unified and relatively straightforward treatment.
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2199-3130 developments over the last 15 years.Includes both theory an.This book presents broadly applicable methods for the large deviation and moderate deviation analysis of discrete and continuous time stochastic systems. A feature of the book is the systematic use of variational representations for quanti
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