LEADER 04482nam 22006972 450 001 9910781947603321 005 20151005020622.0 010 $a1-139-17959-4 010 $a1-316-08837-5 010 $a1-283-38246-6 010 $a9786613382467 010 $a1-139-18930-1 010 $a0-511-97777-8 010 $a1-139-18800-3 010 $a1-139-19060-1 010 $a1-139-18338-9 010 $a1-139-18569-1 035 $a(CKB)2550000000075767 035 $a(EBL)807304 035 $a(OCoLC)782877024 035 $a(SSID)ssj0000571172 035 $a(PQKBManifestationID)11390484 035 $a(PQKBTitleCode)TC0000571172 035 $a(PQKBWorkID)10611696 035 $a(PQKB)10669170 035 $a(UkCbUP)CR9780511977770 035 $a(Au-PeEL)EBL807304 035 $a(CaPaEBR)ebr10520981 035 $a(CaONFJC)MIL338246 035 $z(PPN)261372416 035 $a(MiAaPQ)EBC807304 035 $a(PPN)16585135X 035 $a(EXLCZ)992550000000075767 100 $a20101013d2012|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProbability, random processes, and statistical analysis /$fHisashi Kobayashi, Brian L. Mark, William Turin$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2012. 215 $a1 online resource (xxxi, 780 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a1-139-18079-7 311 $a0-521-89544-8 320 $aIncludes bibliographical references and index. 327 $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. 330 $aTogether 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. 517 3 $aProbability, Random Processes, & Statistical Analysis 606 $aStochastic analysis 615 0$aStochastic analysis. 676 $a519.2/2 700 $aKobayashi$b Hisashi$025536 702 $aMark$b Brian L$g(Brian Lai-bue),$f1969- 702 $aTurin$b William 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910781947603321 996 $aProbability, random processes, and statistical analysis$93724385 997 $aUNINA