Large Deviation Techniques in Decision, Simulation, and Estimation (Wiley Series in Probability and Statistics) - Hardcover

Über die Autorin bzw. den Autor

About the author JAMES A. BUCKLEW is a professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. He received his PhD in electrical engineering from Purdue in 1979. In 1984 Dr. Bucklew received a Presidential Young Investigator Award. He is currently an associate editor of the IEEE Transactions on Information Theory.

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Aus dem Klappentext

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. For example, it gives:

• New analysis and design techniques for hypothesis testing (signal detection) systems
• A new methodology, which is shown to be uniquely optimal, for the simulation of certain classes of rare events
• A proof based entirely upon large deviation theory of the source coding with respect to a fidelity criterion theorem of Shannon
• New expositions and explanations of many standard large deviation theory results
• An overview of some crucial but little known optimality results for parameter estimators

The first part of the text is a heuristic overview and introduction to the major themes of large deviation theory. The second part is concerned with applications of the theory to specific problems in hypothesis testing, simulation, parameter estimation, and information theory. Each chapter has many examples, sample calculations, and extensive exercises at the end, with complete solutions given in the appendix. This is the only readable, mathematically nonrigorous probability book. Large Deviation Techniques in Decision, Simulation, and Estimation is excellent for electrical engineers in academia involved in communications, information, and stochastic control theory, for industrial engineers and computer scientists concerned with simulation techniques, for statisticians interested in hypothesis testing and parameter estimation, and for mathematicians.