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 estimatorsThe 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.