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Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance [Hardcover]

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  • Category: Books (Mathematics)
  • Author:  Kobayashi, Hisashi, Mark, Brian L., Turin, William
  • Author:  Kobayashi, Hisashi, Mark, Brian L., Turin, William
  • ISBN-10:  0521895448
  • ISBN-10:  0521895448
  • ISBN-13:  9780521895446
  • ISBN-13:  9780521895446
  • Publisher:  Cambridge University Press
  • Publisher:  Cambridge University Press
  • Pages:  812
  • Pages:  812
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-May-2011
  • Pub Date:  01-May-2011
  • SKU:  0521895448-11-MPOD
  • SKU:  0521895448-11-MPOD
  • Item ID: 106419197
  • Seller: ShopSpell
  • Ships in: 2 business days
  • Transit time: Up to 5 business days
  • Delivery by: Oct 03 to Oct 05
  • Notes: Brand New Item. Not shipped to AK, HI, APO, FPO, AE.
Covers the fundamental topics together with advanced theories, including the EM algorithm, hidden Markov models, and queueing and loss systems.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.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.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: