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000840252 010__ $$z  2011041741
000840252 020__ $$z9780521895446
000840252 020__ $$z9781139185691$$q(electronic book)
000840252 035__ $$a(MiAaPQ)EBC807304
000840252 035__ $$a(Au-PeEL)EBL807304
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000840252 035__ $$a(CaONFJC)MIL338246
000840252 035__ $$a(OCoLC)782877024
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000840252 050_4 $$aQA274.2$$b.K63 2012
000840252 08204 $$a519.2/2$$223
000840252 1001_ $$aKobayashi, Hisashi.
000840252 24510 $$aProbability, random processes, and statistical analysis$$h[electronic resource] /$$cHisashi Kobayashi, Brian L. Mark, William Turin.
000840252 260__ $$aCambridge ;$$aNew York :$$bCambridge University Press,$$c2012.
000840252 300__ $$axxxi, 780 p.
000840252 504__ $$aIncludes bibliographical references and index.
000840252 5058_ $$aMachine generated contents note: 1. Introduction; Part I. Probability, Random Variables and Statistics: 2. Probability; 3. Discrete random variables; 4. Continuous random variables; 5. Functions of random variables and their distributions; 6. Fundamentals of statistical analysis; 7. Distributions derived from the normal distribution; Part II. Transform Methods, Bounds and Limits: 8. Moment generating function and characteristic function; 9. Generating function and Laplace transform; 10. Inequalities, bounds and large deviation approximation; 11. Convergence of a sequence of random variables, and the limit theorems; Part III. Random Processes: 12. Random process; 13. Spectral representation of random processes and time series; 14. Poisson process, birth-death process, and renewal process; 15. Discrete-time Markov chains; 16. Semi-Markov processes and continuous-time Markov chains; 17. Random walk, Brownian motion, diffusion and it's processes; Part IV. Statistical Inference: 18. Estimation and decision theory; 19. Estimation algorithms; Part V. Applications and Advanced Topics: 20. Hidden Markov models and applications; 21. Probabilistic models in machine learning; 22. Filtering and prediction of random processes; 23. Queuing and loss models.
000840252 506__ $$aAccess limited to authorized users.
000840252 520__ $$a"Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and It's process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum-Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals"--$$cProvided by publisher.
000840252 650_0 $$aStochastic analysis.
000840252 7001_ $$aMark, Brian L.$$q(Brian Lai-bue),$$d1969-
000840252 7001_ $$aTurin, William.
000840252 852__ $$bebk
000840252 85640 $$3ProQuest Ebook Central Academic Complete$$uhttps://univsouthin.idm.oclc.org/login?url=https://ebookcentral.proquest.com/lib/usiricelib-ebooks/detail.action?docID=807304$$zOnline Access
000840252 909CO $$ooai:library.usi.edu:840252$$pGLOBAL_SET
000840252 980__ $$aEBOOK
000840252 980__ $$aBIB
000840252 982__ $$aEbook
000840252 983__ $$aOnline