Author Description

"Together with the fundamentals of probability, random processes, and statistical analysis, this insightful book also presents a broad range of advanced topics and applications not covered in other textbooks. Advanced topics include: - Bayesian inference and conjugate priors - Chernoff bound and large deviation approximation - Principal component analysis and singular value decomposition - Autoregressive moving average (ARMA) time series - Maximum likelihood estimation and the EM algorithm - Brownian motion, geometric Brownian motion, and Ito process - Black-Scholes differential equation for option pricing - Hidden Markov model (HMM) and estimation algorithms - Bayesian networks and sum-product algorithm - Markov chain Monte Carlo methods - Wiener and Kalman filters - Queueing and loss networks. This book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics, and mathematical finance. With a solutions manual, lecture slides, supplementrary materials, and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals."--