James A. Bucklew

TL;DR: Cramir's Theorem and Extensions Sanov's theorem and the Contraction Principle Gaussian Processes and Wentzell-Freidlin Theory Large Deviations for Markov Processes Applications to Detection Theory Asymptotic Expansions Quick Simulation Applications to Parameter Estimation Applications to Information Theory Appendices Solutions to Exercises.

TL;DR: This book presents a unified theory of rare event simulation and the variance reduction technique known as importance sampling from the point of view of the probabilistic theory of large deviations.

TL;DR: The method of analysis is based on an asymptotic analysis of fixed stepsize adaptive algorithms and gives almost sure results regarding the behavior of the parameter estimates, whereas previous stochastic analyses typically consider mean and mean square behavior.

TL;DR: The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems and yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter.

TL;DR: It is shown how large-deviations theory can be applied to construct an asymptotically efficient simulation distribution for Monte Carlo simulation using the importance sampling technique.