000			04213cam a2200349 a 4500
001			16990442
005			20240818092948.0
008			111005s2012 xxkd frb 001 0 eng d
010			_a 2011041741
020			_a9780521895446 (hardback)
020			_a0521895448 (hardback)
040			_aDLC _beng _cDLC _dBTCTA _dYDXCP _dUKMGB _dCDX _dBDX _dBWX _dC#P _dAU@ _dPUL _dDLC _dEG-ScBUE
082	0	0	_a519.22 _222 _bKOB
100	1		_aKobayashi, Hisashi _93760
245	1	0	_cHisashi Kobayashi, Brian L. Mark, William Turin. _aProbability, random processes, and statistical analysis /
260			_aCambridge, United Kingdom ; _aNew York, United States : _bCambridge University Press, _c2012.
300			_axxxi, 780 p. : _bcharts ; _c26 cm.
500			_aIndex : p. 759-780.
504			_aBibliography : p. [740]-758.
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"-
520			_a"Probability, Random Processes, and Statistical Analysis Together with the fundamentals of probability, random processes, and statistical analysis, this insightful book also presents a broad range of advanced topics and applications not covered in other textbooks. Advanced topics include: - Bayesian inference and conjugate priors-Chernoff bound and large deviation approximation-Principal component analysis and singular value decomposition-Autoregressive moving average (ARMA) time series-Maximum likelihood estimation and the EM algorithm-Brownian motion, geometric Brownian motion, and Ito process-Black-Scholes differential equation for option pricing"-
650		0	_aStochastic analysis _937068
653			_bENGELC _cJanuary2015
655			_vreading book _934232
700			_aMark, Brian L. _q(Brian Lai-bue), _d1969- _93764
700	1		_aTurin, William _937070
942			_2ddc _k519.22 KOB _hRDA-MOD
999			_c18840 _d18812
336			_2rdacontent _atext _btxt
337			_aunmediated _2rdamedia _bn
338			_avolume _bnc _2rdacarrier