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Probability, Random Processes, and Statistical Analysis

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

$80.00 (Z)

textbook
  • Date Published: February 2012
  • availability: In stock
  • format: Hardback
  • isbn: 9780521895446

$80.00 (Z)
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About the Authors
  • 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, 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:

    • Includes key advanced topics not covered in other textbooks, such as the EM algorithm, hidden Markov models, and queueing and loss systems
    • Presents many illustrative examples from areas such as communications, signal processing, network theory and financial engineering
    • Supplementary materials will be provided online, including a solutions manual, lecture slides and MATLAB programs
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    Reviews & endorsements

    "This book provides a very comprehensive, well-written and modern approach to the fundamentals of probability and random processes, together with their applications in the statistical analysis of data and signals. It provides a one-stop, unified treatment that gives the reader an understanding of the models, methodologies and underlying principles behind many of the most important statistical problems arising in engineering and the sciences today." – Dean H. Vincent Poor, Princeton University

    “This is a well-written up-to-date graduate text on probabilty and random processes. It is unique in combining statistical analysis with the probabilistic material. As noted by the authors, the material, as presented, can be used in a variety of current application areas, ranging from communications to bioinformatics. I particularly liked the historical introduction, which should make the field exciting to the student, as well as the introductory chapter on probability, which clearly describes for the student the distinction between the relative frequency and axiomatic approaches to probability. I recommend it unhesitatingly. It deserves to become a leading text in the field.” – Mischa Schwartz, Professor Emeritus, Columbia University

    “Hisashi Kobayashi, Brian L. Mark, and William Turin are highly experienced university teachers and scientists. Based on this background their book covers not only fundamentals but also a large range of applications. Some of them are treated in a textbook for the first time. Without any doubt the book will be extremely valuable to graduate students and to scientists in universities and industry as well. Congratulations to the authors!” – Prof. Dr.-Ing. Eberhard Hänsler, Technische Universität Darmstadt

    “An up-to-date and comprehensive book with all the fundamentals in Probability, Random Processes, Stochastic Analysis, and their interplays and applications, which lays a solid foundation for the students in related areas. It is also an ideal textbook with five relatively independent but logically interconnected parts and the corresponding solution manuals and lecture slides. Furthermore, to my best knowledge, the similar editing in Part IV and Part V can’t be found elsewhere.“ – Zhisheng Niu, Tsinghua University

    "authors have provided a much welcome textbook to our community in the large. The book will be useful to students and researchers in areas as diverse as communications, networks, signal processing, bioinformatics, and econometrics. Given the lucid style of exposition and the readily available online supplementing resources, it is very likely that this text will see its popularity in classroom teaching. I have one copy already, and plan to buy another." - Ou Zhao, Mathematical Reviews

    See more reviews

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    Product details

    • Date Published: February 2012
    • format: Hardback
    • isbn: 9780521895446
    • length: 812 pages
    • dimensions: 253 x 178 x 40 mm
    • weight: 1.7kg
    • contains: 114 b/w illus. 11 tables 458 exercises
    • availability: In stock
  • Table of Contents

    1. Introduction
    Part I. Probability, Random Variables and Statistics:
    2. Probability
    3. Discrete random variables
    4. Continuous random variables
    5. Functions of random variables and their distributions
    6. Fundamentals of statistical analysis
    7. Distributions derived from the normal distribution
    Part II. Transform Methods, Bounds and Limits:
    8. Moment generating function and characteristic function
    9. Generating function and Laplace transform
    10. Inequalities, bounds and large deviation approximation
    11. Convergence of a sequence of random variables, and the limit theorems
    Part III. Random Processes:
    12. Random process
    13. Spectral representation of random processes and time series
    14. Poisson process, birth-death process, and renewal process
    15. Discrete-time Markov chains
    16. Semi-Markov processes and continuous-time Markov chains
    17. Random walk, Brownian motion, diffusion and itô processes
    Part IV. Statistical Inference:
    18. Estimation and decision theory
    19. Estimation algorithms
    Part V. Applications and Advanced Topics:
    20. Hidden Markov models and applications
    21. Probabilistic models in machine learning
    22. Filtering and prediction of random processes
    23. Queuing and loss models.

  • general resources

    instructor resources

    View all resources
    Group Section Name Type Size Sort Order filter vars
    General ResourcesSupplemetary Materialspdf371KB0 general resources general resources general resourcesgeneral resources
    General ResourcesSolutionsSolutions to Starred Problemspdf456KB1solutions general resources solutions general resourcessolutions
    General ResourcesErrataErratapdf164KB2errata general resources errata general resourceserrata
    Instructor ResourcesSolutionsSolutions for Instructorspdf1226KB0solutions instructor resources solutions instructor resourcessolutions

    This title has a locked file and access is given only to instructors adopting the textbook for their class. We need to strictly enforce this so that solutions are not made available to students. To gain access to locked resources you need to first log in with your Cambridge account details and then return to this page to submit details of your course so you can be authenticated as an instructor. Click here to log in. If you do not have a Cambridge account you will first need to click here to create an account and then return to this page to be authenticated.


    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

    If you are having problems accessing these resources please email cflack@cambridge.org

  • Authors

    Hisashi Kobayashi, Princeton University, New Jersey
    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, George Mason University, Virginia
    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, AT&T Bell Laboratories, New Jersey
    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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