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Bivariate Markov Processes and Their Estimation (Paperback): Yariv Ephraim, Brian L. Mark Bivariate Markov Processes and Their Estimation (Paperback)
Yariv Ephraim, Brian L. Mark
R1,753 Discovery Miles 17 530 Ships in 10 - 15 working days

Bivariate Markov processes play a central role in the theory and applications of estimation, control, queuing, biomedical engineering, and reliability. Bivariate Markov Processes and Their Estimation presents some of the fundamentals of the theory of bivariate Markov processes, and reviews the various parameters and signal estimation approaches that are associated with these Markov processes. It reviews both causal and non-causal estimation of some statistics of the bivariate Markov processes. In addition, it covers off-line as well as on-line recursive parameter estimation approaches. Bivariate Markov Processes and Their Estimation is an ideal springboard for researchers and students who are interested in pursuing the study of this interesting family of processes. While proofs are generally omitted, an interested reader should be able to implement the estimation algorithms for bivariate Markov chains directly from the text. The material should be accessible to the signal processing community, although it requires some familiarity with Markov chains and the intricacies of the theory of hidden Markov models.

Probability, Random Processes, and Statistical Analysis - Applications to Communications, Signal Processing, Queueing Theory... Probability, Random Processes, and Statistical Analysis - Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance (Hardcover)
Hisashi Kobayashi, Brian L. Mark, William Turin
R2,341 Discovery Miles 23 410 Ships in 12 - 19 working days

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 Ito 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:

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