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A Weak Convergence Approach to the Theory of Large Deviations by Paul Dupuis (En

Description: A Weak Convergence Approach to the Theory of Large Deviations by Paul Dupuis, Richard S. Ellis Applies the well-developed tools of the theory of weak convergence of probability measures to large deviation analysis--a consistent new approach The theory of large deviations, one of the most dynamic topics in probability today, studies rare events in stochastic systems. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description Applies the well-developed tools of the theory of weak convergenceof probability measures to large deviation analysis--a consistentnew approach The theory of large deviations, one of the most dynamic topics inprobability today, studies rare events in stochastic systems. Thenonlinear nature of the theory contributes both to its richness anddifficulty. This innovative text demonstrates how to employ thewell-established linear techniques of weak convergence theory toprove large deviation results. Beginning with a step-by-stepdevelopment of the approach, the book skillfully guides readersthrough models of increasing complexity covering a wide variety ofrandom variable-level and process-level problems. Representationformulas for large deviation-type expectations are a key tool andare developed systematically for discrete-time problems. Accessible to anyone who has a knowledge of measure theory andmeasure-theoretic probability, A Weak Convergence Approach to theTheory of Large Deviations is important reading for both studentsand researchers. Back Cover Applies the well-developed tools of the theory of weak convergence of probability measures to large deviation analysis--a consistent new approach The theory of large deviations, one of the most dynamic topics in probability today, studies rare events in stochastic systems. The nonlinear nature of the theory contributes both to its richness and difficulty. This innovative text demonstrates how to employ the well-established linear techniques of weak convergence theory to prove large deviation results. Beginning with a step-by-step development of the approach, the book skillfully guides readers through models of increasing complexity covering a wide variety of random variable-level and process-level problems. Representation formulas for large deviation-type expectations are a key tool and are developed systematically for discrete-time problems. Accessible to anyone who has a knowledge of measure theory and measure-theoretic probability, A Weak Convergence Approach to the Theory of Large Deviations is important reading for both students and researchers. Flap Applies the well-developed tools of the theory of weak convergence of probability measures to large deviation analysis--a consistent new approach The theory of large deviations, one of the most dynamic topics in probability today, studies rare events in stochastic systems. The nonlinear nature of the theory contributes both to its richness and difficulty. This innovative text demonstrates how to employ the well-established linear techniques of weak convergence theory to prove large deviation results. Beginning with a step-by-step development of the approach, the book skillfully guides readers through models of increasing complexity covering a wide variety of random variable-level and process-level problems. Representation formulas for large deviation-type expectations are a key tool and are developed systematically for discrete-time problems. Accessible to anyone who has a knowledge of measure theory and measure-theoretic probability, A Weak Convergence Approach to the Theory of Large Deviations is important reading for both students and researchers. Author Biography PAUL DUPUIS is a professor in the Division of Applied Mathematics at Brown University in Providence, Rhode Island.RICHARD S. ELLIS is a professor in the Department of Mathematics and Statistics at the University of Massachusetts at Amherst. Table of Contents Formulation of Large Deviation Theory in Terms of the LaplacePrinciple. First Example: Sanovs Theorem. Second Example: Mogulskiis Theorem. Representation Formulas for Other Stochastic Processes. Compactness and Limit Properties for the Random Walk Model. Laplace Principle for the Random Walk Model with ContinuousStatistics. Laplace Principle for the Random Walk Model with DiscontinuousStatistics. Laplace Principle for the Empirical Measures of a MarkovChain. Extensions of the Laplace Principle for the Empirical Measures of aMarkov Chain. Laplace Principle for Continuous-Time Markov Processes withContinuous Statistics. Appendices. Bibliography. Indexes. Long Description Applies the well-developed tools of the theory of weak convergence of probability measures to large deviation analysis a consistent new approach The theory of large deviations, one of the most dynamic topics in probability today, studies rare events in stochastic systems. The nonlinear nature of the theory contributes both to its richness and difficulty. This innovative text demonstrates how to employ the well-established linear techniques of weak convergence theory to prove large deviation results. Beginning with a step-by-step development of the approach, the book skillfully guides readers through models of increasing complexity covering a wide variety of random variable-level and process-level problems. Representation formulas for large deviation-type expectations are a key tool and are developed systematically for discrete-time problems. Accessible to anyone who has a knowledge of measure theory and measure-theoretic probability, A Weak Convergence Approach to the Theory of Large Deviations is important reading for both students and researchers. Feature Contains detailed appendices and indices. Representation formulas provide a link to other topics of interest, such as risk-sensitive control and robust control, Doons h-transform, and the construction of conditioned processes. Details ISBN0471076724 Author Richard S. Ellis Short Title WEAK CONVERGENCE APPROACH TO T Language English ISBN-10 0471076724 ISBN-13 9780471076728 Media Book Format Hardcover Illustrations Yes Year 1997 Place of Publication New York Country of Publication United States Affiliation Brown Univ., Providence, Rhode Island Pages 504 Edition 1st DOI 10.1604/9780471076728 Series Number 313 UK Release Date 1997-03-10 AU Release Date 1997-02-13 NZ Release Date 1997-02-13 Publisher John Wiley & Sons Inc Series Wiley Series in Probability and Statistics Publication Date 1997-03-10 Imprint Wiley-Interscience DEWEY 519.534 Audience Postgraduate, Research & Scholarly US Release Date 1997-03-10 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:126565225;

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A Weak Convergence Approach to the Theory of Large Deviations by Paul Dupuis (En

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ISBN-13: 9780471076728

Book Title: A Weak Convergence Approach to the Theory of Large Deviations

Item Height: 241 mm

Item Width: 162 mm

Author: Richard S. Ellis, Paul Dupuis

Publication Name: A Weak Convergence Approach to the Theory of Large Deviations

Format: Hardcover

Language: English

Publisher: John Wiley & Sons Inc

Subject: Mathematics

Publication Year: 1997

Type: Textbook

Item Weight: 916 g

Number of Pages: 504 Pages

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