A Weak Convergence Approach to the Theory of Large Deviations

Paul Dupuis, Richard S. Ellis

ISBN 10: 0471076724 / ISBN 13: 9780471076728

Published by John Wiley and Sons Inc, US, 1997

About this Item

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.

Seller Inventory # LU-9780471076728

Bibliographic Details

• Title: A Weak Convergence Approach to the Theory of Large Deviations
• Publisher: John Wiley and Sons Inc, US
• Publication Date: 1997
• Language: English
• ISBN 10: 0471076724
• ISBN 13: 9780471076728
• Binding: Hardback
• Edition: 1st.
• Condition: New
• Dimensions: 16.5W x 2.8H x 24D centimeters 16.5 centimeters width by 2.8 centimeters height by 24 centimeters depth
• Weight: 879 grams

About this title

Synopsis

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.

About the Author

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.