Hisashi Kobayashi

Probability, Random Processes, and Statistical Analysis (eBook, ePUB)

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

• Format: ePub

Produktbeschreibung

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, 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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 15. Dezember 2011
• Englisch
• ISBN-13: 9781139179591
• Artikelnr.: 41329503

Autorenporträt

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.

Inhaltsangabe

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.