A Weak Convergence Approach to the Theory of Large Deviations. Este artículo no está disponible.
Idioma: inglés
Editorial: Wiley-Interscience, 1997
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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.
N° de ref. del artículo 00106583609
- Título
- A Weak Convergence Approach to the Theory of Large Deviations
- Autor
- Paul Dupuis
- Editorial
- Wiley-Interscience
- Año de publicación
- 1997
- Estado
- Good
- Encuadernación
- Hardback
- Idioma
- inglés
- ISBN 10
- 0471076724
- ISBN 13
- 9780471076728
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.
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.
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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.
RICHARD S. ELLIS is a professor in the Department of Mathematics and Statistics at the University of Massachusetts at Amherst.
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