000			03628nam a2200337 a 4500
001			ebr10520981
003			CaPaEBR
006			m u
007			cr cn|||||||||
008			111005s2012 enk sb 001 0 eng d
010			_z 2011041741
020			_z9780521895446 (hardback)
020			_z9781139185691 (e-book)
040			_aCaPaEBR _cCaPaEBR
035			_a(OCoLC)772000569
050	1	4	_aQA274.2 _b.K63 2012eb
082	0	4	_a519.2/2 _223
100	1		_aKobayashi, Hisashi.
245	1	0	_aProbability, random processes, and statistical analysis _h[electronic resource] / _cHisashi Kobayashi, Brian L. Mark, William Turin.
260			_aCambridge ; _aNew York : _bCambridge University Press, _c2012.
300			_axxxi, 780 p.
504			_aIncludes bibliographical references and index.
505	8		_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.
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.
533			_aElectronic reproduction. _bPalo Alto, Calif. : _cebrary, _d2012. _nAvailable via World Wide Web. _nAccess may be limited to ebrary affiliated libraries.
650		0	_aStochastic analysis.
655		7	_aElectronic books. _2local
700	1		_aMark, Brian L. _q(Brian Lai-bue), _d1969-
700	1		_aTurin, William.
710	2		_aebrary, Inc.
856	4	0	_uhttp://site.ebrary.com/lib/daystar/Doc?id=10520981 _zAn electronic book accessible through the World Wide Web; click to view
999			_c196663 _d196663