Large deviation techniques in decision, simulation, and estimation
By: James A. Bucklew.
Edition: First edition
Series: Wiley series in probability and mathematical statistics.
Publisher: Wiley
Published: 1990
Place of publication: New York
Language: eng
Format: Hardcover
Number of pages: 290
ISBN-13: 9780471618560
Copyright date: ©1990
Subjects: Large deviations.
Description: Large deviation theory is a branch of probability concerned with explaining the behavior of certain types of rare events. Large Deviation Techniques in Decision, Simulation, and Estimation is an introductory level exposition for a nonmathematical audience of the major results and techniques available in this area. It is excellent for applied statisticians, communications engineers, statistical signal processors, information theorists, and even large deviation theorists interested in the major application areas of their field. Applications of large deviation theory are stressed throughout with entire chapters devoted to hypothesis testing, parameter estimation, fast simulation methodologies, and information theory. In a relaxed fashion, it introduces most of the major ideas and models of the subject. In addition, several new results are presented in various application areas.
Notes: Includes bibliographical references (p. 261-266) and index.
"A Wiley-Interscience publication."
Table of contents: title: - Cramir's Theorem and Extensions;; title: - Sanov's Theorem and the Contraction Principle;; title: - Gaussian Processes and Wentzell-Freidlin Theory;; title: - Large Deviations for Markov Processes;; title: - Applications to Detection Theory;; title: - Asymptotic Expansions;; title: - Quick Simulation;; title: - Applications to Parameter Estimation;; title: - Applications to Information Theory;; title: - Appendices;; title: - Solutions to Exercises;; title: - References;; title: - Index.