Probability, Random Processes, and Statistical Analysis

Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance

Nonfiction, Science & Nature, Technology, Engineering, Mathematics
Cover of the book Probability, Random Processes, and Statistical Analysis by Hisashi Kobayashi, Brian L. Mark, William Turin, Cambridge University Press
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Author: Hisashi Kobayashi, Brian L. Mark, William Turin ISBN: 9781139179591
Publisher: Cambridge University Press Publication: December 15, 2011
Imprint: Cambridge University Press Language: English
Author: Hisashi Kobayashi, Brian L. Mark, William Turin
ISBN: 9781139179591
Publisher: Cambridge University Press
Publication: December 15, 2011
Imprint: Cambridge University Press
Language: English

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 Itô process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum–Welch 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 Itô process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum–Welch 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.

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