This book presents an introduction to the theory of large deviations. Large deviation estimates have proved to be the crucial tool required to handle many questions in statistics, engineering, statistial mechanics, and applied probability. The mathematics is rigorous and the applications come from a wide range of areas, including electrical engineering and DNA sequences. The second edition includes new material on concentration inequalities and the metric and weak convergence approaches to large deviations. General statements and applications have been sharpened, new exercises added, and the bibliography updated. Amir Dembo is Associate Professor of Mathematics and Statistics at Stanford University, and Professor of Electrical Engineering at the Technion-Israel Institute of Technology. He currently serves on the editorial board of the Annals of Probability. Ofer Zeitouni is Professor of Electrical Engineering at the Technion- Israel Institute of Technology. He has served on the editorial board of the IEEE Transactions on Information Theory and currently serves on the editorial board of Stochastic Processes and Applications.

Large deviation estimates have proved to be a crucial tool in statistics, engineering, statistical mechanics, and applied probability. This new edition presents an introduction to the theory of large deviations, with rigorous mathematical applications from a wide range of areas, including electrical engineering and DNA sequencing. 34 illus.

Introduction.- LDP for Finite Dimensional Space.- Applications - The Finite Dimensional Case.- General Principles.- Sample Path Large Deviations.- The LDP for Abstract Empirical Measures.- Applications of Empirical Measures LDP.