Covers the fundamental topics together with advanced theories, including the EM algorithm, hidden Markov models, and queueing and loss systems.
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Hisashi Kobayashi is the Sherman Fairchild University Professor Emeritus at Princeton University, where he was previously Dean of the School of Engineering and Applied Science. He also spent 15 years at the IBM Research Center, Yorktown Heights, NY, and was the Founding Director of the IBM Tokyo Research Laboratory. He is an IEEE Life Fellow, an IEICE Fellow, was elected to the Engineering Academy of Japan (1992) and received the 2005 Eduard Rhein Technology Award.
Brian L. Mark is a Professor in the Department of Electrical and Computer Engineering at George Mason University. Prior to this, he was a Research Staff Member at the NEC C&C Research Laboratories in Princeton, New Jersey and in 2002 he received a National Science Foundation CAREER award.
William Turin is currently a Consultant at AT&T Labs Research. As a Member of Technical Staff at AT&T Bell Laboratories and later a Technology Consultant at AT&T Labs Research for 21 years, he developed methods for qualifying the performance of communication systems. He is the author of six books and numerous papers.
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Hardcover. Condition: new. Hardcover. 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 BaumWelch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks 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. Together with the fundamental topics, this book covers advanced theories and engineering applications, including the EM algorithm, hidden Markov models, and queueing and loss systems. A solutions manual, lecture slides and MATLAB programs all available online make this ideal for classroom teaching as well as a valuable reference for professionals. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780521895446
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Hardcover. Condition: new. Hardcover. 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 BaumWelch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks 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. Together with the fundamental topics, this book covers advanced theories and engineering applications, including the EM algorithm, hidden Markov models, and queueing and loss systems. A solutions manual, lecture slides and MATLAB programs all available online make this ideal for classroom teaching as well as a valuable reference for professionals. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9780521895446
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Hardcover. Condition: new. Hardcover. 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 BaumWelch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks 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. Together with the fundamental topics, this book covers advanced theories and engineering applications, including the EM algorithm, hidden Markov models, and queueing and loss systems. A solutions manual, lecture slides and MATLAB programs all available online make this ideal for classroom teaching as well as a valuable reference for professionals. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9780521895446
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