000 04277cam a2200337 i 4500
001 16990442
003 EG-ScBUE
005 20241107155644.0
008 111005s2012 enkd f b 001 0 eng d
020 _a9780521895446 (hardback)
020 _a0521895448 (hardback)
040 _aDLC
_beng
_erda
_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,
_eauthor.
_93760
245 1 0 _aProbability, random processes, and statistical analysis /
_cHisashi Kobayashi, Princeton University, Brian L. Mark, George Mason University, William Turin, At&T Labs Research.
264 _aCambridge, United Kingdom ;
_aNew York, United States :
_bCambridge University Press,
_c2012.
300 _axxxi, 780 pages :
_bcharts ;
_c26 cm
336 _2rdacontent
_atext
_btxt
337 _aunmediated
_2rdamedia
_bn
338 _avolume
_bnc
_2rdacarrier
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"-
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 7 _aStochastic analysis.
_2BUEsh
_937068
653 _bENGELC
_cJanuary2015
655 _vReading book
_934232
700 1 _aMark, Brian L.
_q(Brian Lai-bue),
_d1969-
_eauthor.
_93764
700 1 _aTurin, William,
_eauthor.
_937070
942 _2ddc
_cBB
999 _c18840
_d18812