Probability Random Processes and Statistical Analysis

Together with the fundamentals of probability random processes and statistical analysis this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics time series and spectral representation inequalities bound and approximation maximum-likelihood estimation and the expectation-maximization (EM) algorithm geometric Brownian motion and It process. Applications such as hidden Markov models (HMM) the Viterbi BCJR and Baum-Welch algorithms algorithms for machine learning Wiener and Kalman filters queueing and loss networks and are treated in detail. The 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 supplementary materials and MATLAB programs all available online it is ideal for classroom teaching as well as a valuable reference for professionals. Professor Hisashi Kobayashi discusses the book: