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Cambridge University Press
Online publication date:
June 2012
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Book description

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 Itô 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.


'This book provides a very comprehensive, well-written and modern approach to the fundamentals of probability and random processes, together with their applications in the statistical analysis of data and signals. … It provides a one-stop, unified treatment that gives the reader an understanding of the models, methodologies and underlying principles behind many of the most important statistical problems arising in engineering and the sciences today.'

Dean H. Vincent Poor - Princeton University

'This is a well-written up-to-date graduate text on probabilty and random processes. It is unique in combining statistical analysis with the probabilistic material. As noted by the authors, the material, as presented, can be used in a variety of current application areas, ranging from communications to bioinformatics. I particularly liked the historical introduction, which should make the field exciting to the student, as well as the introductory chapter on probability, which clearly describes for the student the distinction between the relative frequency and axiomatic approaches to probability. I recommend it unhesitatingly. It deserves to become a leading text in the field.'

Professor Emeritus Mischa Schwartz - Columbia University

'Hisashi Kobayashi, Brian L. Mark, and William Turin are highly experienced university teachers and scientists. Based on this background their book covers not only fundamentals but also a large range of applications. Some of them are treated in a textbook for the first time. … Without any doubt the book will be extremely valuable to graduate students and to scientists in universities and industry as well. Congratulations to the authors!'

Prof. Dr.-Ing. Eberhard Hänsler - Technische Universität Darmstadt

'An up-to-date and comprehensive book with all the fundamentals in Probability, Random Processes, Stochastic Analysis, and their interplays and applications, which lays a solid foundation for the students in related areas. It is also an ideal textbook with five relatively independent but logically interconnected parts and the corresponding solution manuals and lecture slides. Furthermore, to my best knowledge, the similar editing in Part IV and Part V can’t be found elsewhere.'

Zhisheng Niu - Tsinghua University

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[1] J., Abate, G. L., Choudhury, and W., Whitt. Numerical inversion of multidimensional Laplace transforms by the Laguerre method. Performance Evaluation, 31: (1998), 3 & 4, 229–243. (Cited on p. 234.)
[2] J., Abate and W., Whitt. Numerical inversion of Laplace transforms of probability distributions. ORSA Journal on Computing, 7:1 (1995), 36–40. (Cited on p. 234.)
[3] M., Abramowitz and I. A., Stegun. Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables (New York: Dover, 1977). (Cited on p. 261.)
[4] H., Akimaru and K., Kawashima. Teletraffic: Fundamentals and Applications (Tokyo: Institute of Electrical Communications, 1990). (In Japanese.) (Cited on p. 707.)
[5] R., Arens. Complex process for envelopes of normal noise. IRE Transactions on Information Theory, IT-3 (1957), 204–207. (Cited on p. 340.)
[6] S., Asmussen. Applied Probability and Queues (New York: Springer, 2003). (Cited on p. 268.)
[7] S., Asmussen, O., Nerman, and M., Olsson. Fitting phase-type distributions via the EM algorithm. Scandinavian Journal of Statistics, 23:4 (1996), 419–441. (Cited on pp. 566, 606.)
[8] K., Azuma. Weighted sums of certain dependent random variables. Tohoku Mathematics Journal, 19:3 (1967), 357–367. (Cited on p. 272.)
[9] L., Bachelier. Théorie de la spéculation. Annales scientifiques de l'École Normale Supérieure, 3e série, 17 (1900), 21–86. (Cited on pp. 5, 11.)
[10] L., Bachelier. Louis Bachelier's Theory of Speculation: The Origins of Modern Finance (Translated and with Commentary by Mark, Davis and Alison, Etheridge) (Princeton, NJ: Princeton University Press, 2007). (Cited on pp. 5, 11, 516.)
[11] L. R., Bahl, J., Cocke, F., Jelinek, and J., Raviv. Optimal decoding of linear codes for minimizing symbol error rate. IEEE Transactions on Information Theory, IT-20 (1974), 284–287. (Cited on pp. 593, 605.)
[12] F. G., Ball, R. K., Milne, and G. F., Yeo. Continuous-time Markov chains in a random environment, with applications to ion channel modelling. Advances in Applied Probability, 26:4 (1994), 919–946. (Cited on p. 477.)
[13] F. G., Ball, R. K., Milne, and G. F., Yeo. Marked continuous-time Markov chain modelling of burst behaviour for single ion channels. Journal of Applied Mathematics and Decision Sciences, (2007), 1–14. (Cited on p. 477.)
[14] G. P., Barsharin, A. N., Langville, and V. A., Naumov. The life and work of A. A. Markov. Linear Algebra and Its Applications, 386 (2004), 3–26. (Cited on pp. 319, 478.)
[15] L. E., Baum and T., Petrie. Statistical inference for probabilistic functions of finite state Markov chains. Annals of Mathematical Statististics, 37 (1966), 1559–1563. (Cited on p. 605.)
[16] L. E., Baum, T., Petrie, G., Soules, and N., Weiss. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics, 41:1 (1970), 164–171. (Cited on pp. 564, 605, 606.)
[17] R. E., Bellman. Dynamic Programming (Princeton, NJ: Princeton University Press, 1957). (Cited on p. 592.)
[18] J. O., Berger. Statistical Decision Theory and Bayesian Analysis, 2nd edition (Springer-Verlag, 1995). (Cited on p. 104.)
[19] J. M., Bernardo and A. F. M., Smith. Bayesian Theory (Wiley, 1994). (Cited on p. 104.)
[20] P. L., Bernstein. Against the Gods: The Remarkable Story of Risk (John Wiley & Sons, 1996). (Cited on p. 12.)
[21] S. N., Bernstein. On certain modifications of Chebyshev's inequality. Doklady Akademii Nauk USSR, 17:6 (1937), 275–277. (Cited on p. 271.)
[22] C., Berrou and A., Glavieux. Near optimum error correcting coding and decoding: Turbo codes. IEEE Transactions on Communications, 44 (1996), 1261–1271. (Cited on p. 605.)
[23] M., Berry, S., Dumais, and G., O'Brien. Using linear algebra for intelligent information retrieval. SIAM Review, 37:4 (1995), 573–595. (Cited on pp. 372, 395.)
[24] D., Bertsekas and J. N., Tsitsiklis, Introduction to Probability, 2nd edition (Athena Scientific, 2008). (Cited on p. 37.)
[25] P. J., Bickel and K. A., Doksum. Mathematical Statistics: Basic Ideas and Selected Topics, Vol. 1 (Prentice Hall, 2001). (Cited on pp. 526, 536, 549.)
[26] P., Billingsley. Probability and Measure, 3rd edn (John Wiley & Sons, 1995). (Cited on pp. 293, 305, 308.)
[27] P., Billingsley. Convergence of Probability Measures (New York: Wiley, 1999). (Cited on p. 202.)
[28] G., Birkhoff and S., MacLane. A Survey of Modern Algebra, 5th edn (New York: Macmillan, 1996). (Cited on p. 241.)
[29] F., Black and M., Scholes. The pricing of options and corporate liabilities. Journal of Political Economy, 81 (1973), 637–654. (Cited on pp. 5, 511.)
[30] R. B., Blackman and J. W., Tukey. The Measurement of Power Spectra, from the Point of View of Communication Engineering (New York: Dover, 1959). (Cited on p. 357.)
[31] I. F., Blake. An Introduction to Applied Probability (New York: John Wiley & Sons, Inc., 1979). (Cited on p. 37.)
[32] R., Blossey. Statistical Mechanics for Biologists (Chapman & Hall/CRC, 2006). (Cited on p. viii.) xxviii.)
[33] G., Bolch, S., Greiner, H., de Meer, and K. S., Trivedi. Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications (John Wiley & Sons, 2006). (Cited on p. 731.)
[34] E., Borel. Les probabilités dénombrables et leurs applications arithmétiques. Rendiconti del Circolo Matematico di Palermo, 27 (1909), 247–271. (Cited on p. 300.)
[35] L., Breiman. Probability (Reading, MA: Addison-Wesley, 1968). (Cited on pp. 308, 516.)
[36] L., Breiman. Probability and Stochastic Processes with a View Toward Applications (Boston: Houghton Mifflin, 1969). (Cited on p. 690.)
[37] L., Bresslau, P., Cao, L., Fan, G., Phillips, and S., Shenker. Web caching and Zipf-like distributions: Evidence and implications. In Proceedings of INFOCOM'99, pp. 126–134 (1999). (Cited on p. 63.)
[38] L., Breuer. An EM algorithm for batch Markovian arrival processes and its comparison to simpler estimation procedure. Annals of Operations Research, 112 (2002), 123–138. (Cited on pp. 566, 606.)
[39] M. P., Brown, R., Hughey, A., Kroghet al.Using Dirichlet mixture priors to derive hidden Markov models for protein families. In Proceedings of First International Conference on Intelligent Systems for Molecular Biology, L., Hunter, D., Searts and J., Shavlile (eds), pp. 47–55 (1993). (Cited on p. 605.)
[40] S. L., Brumelle. Some inequalities for parallel server queues. Operations Research, 19:2 (1971), 402–413. (Cited on p. 730.)
[41] D., Bryant, N., Galtier, and M. A., Poursat. Likelihood calculation in molecular phylogenetics. In O., Gascuel, ed., Mathematics of Evolution and Phylogeny (Oxford University Press, 2004). (Cited on pp. 476, 477.)
[42] J. A., Bucklew. Large Deviation Techniques in Decision, Simulation and Estimation (New York: John Wiley & Sons, Inc., 1990). (Cited on p. 268.)
[43] C. J., Burke and M. A., Rosenblatt. Markovian function of a Markov chain. Annals of Mathematical Statistics, 29:4 (1958), 1112–1122. (Cited on p. 605.)
[44] P. I., Butzer, P. J. S. G., Ferreira, J. R., Higginset al.Interpolation and sampling: E.T. Whittaker, K. Ogura and their followers. Journal of Fourier Analysis and Applications, Online FirstK™, 2010. (Cited on p. 353.)
[45] F. A., Campbell and R. M., Foster. Fourier Integrals for Practical Applications (Princeton, NJ: Van Nostrand Company, Inc., 1948). (Cited on p. 194.)
[46] O., Cappé, E., Moulines, and T., Rydén. Inference in Hidden Markov Models (Springer, 2005). (Cited on p. 606.)
[47] K. M., Chandy, U., Herzog, and L., Woo. Parametric analysis of queuing networks. IBM Journal of Research and Development, 19:1 (1975), 43–49. (Cited on p. 710.)
[48] K. M., Chandy and C. H., Sauer. Computational algorithms for product form queueing networks. Communications of the ACM, 23:10 (1980), 573–583. (Cited on p. 731.)
[49] R. W., Chang and J. C., Hancock. On receiver structures for channels having memory. IEEE Transactions on Information Theory, IT-12:4 (1966), 463–468. (Cited on p. 605.)
[50] E., Charniak and R. P., Goldman. A semantics for probabilistic quantifier-free first-order languages with particular application to story understanding. In Proceedings of the 1989 International Joint Conference on Artificial Intelligence, pp. 1074–1079 (1989). (Cited on pp. 615, 624.)
[51] C., Chatfield. The Analysis of Time Series: Theory and Practice (London: Chapman & Hall, 1975). (Cited on p. 357.)
[52] K. L., Chung. Markov Chains: With Stationary Transition Probabilities (New York: Springer-Verlag, 1967). (Cited on p. 477.)
[53] K. L., Chung. A Course in Probability Theory (New York: Bruce & World, 1968). (Cited on p. 202.)
[54] K. L., Chung. A Course in Probability Theory, 2nd edn (New York: Academic Press, 1974). (Cited on p. 308.)
[55] R. D., Cideciyan, F., Dolivo, R., Hermann, W., Hirt, and W., Schott. A PRML system for digital magnetic recording. IEEE Journal of Special Areas in Communications, JSAC-10:1 (1992), 3856. (Cited on p. 592.)
[56] E., Çinlar. Markov renewal theory. Advances in Applied Probability, 1 (1969), 123–187. (Cited on pp. 457, 477.)
[57] E., Çinlar. Introduction to Stochastic Processes (Englewood Cliffs, NJ: Prentice Hall, 1975). (Cited on pp. 418, 451, 477.)
[58] E., Çinlar. Markov renewal theory: a survey. Management Science, 21:7 (1975), 727–752. (Cited on pp. 457, 477, 478.)
[59] E. G., Coffman, R. R., Muntz, and H., Trotter. Waiting time distributions for processorsharing systems. Journal of ACM, 17:1 (1970), 123–130. (Cited on p. 717.)
[60] I., Cohen, N., Sebe, F. G., Cozman, M. C., Cirelo, and T. S., Huang. Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data. In Proceedings 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I-595–I-601 (2003). (Cited on pp. 615, 624.)
[61] J. W., Cohen. The Single Server Queue, 2nd edn (Amsterdam: North-Holland, 1982). (Cited on p. 730.)
[62] D., Colquhoun and A. G., Hawkes. Relaxation and fluctuations of membrane currents that flow through drug-operated channels. Proceedings of the Royal Society of London, Series B, 199 (1977), 231–262. (Cited on pp. 461, 466, 477.)
[63] D., Colquhoun and A. G., Hawkes. On the stochastic properties of single ion channels. Proceedings of the Royal Society of London, Series B, 211 (1981), 205–235. (Cited on p. 477.)
[64] D., Colquhoun and A. G., Hawkes. On the stochastic properties of bursts of single ion channel openings and of clusters of bursts. Proceedings of the Royal Society of London, Series B, 300 (1982), 1–59. (Cited on p. 477.)
[65] J. W., Cooley, P. A. W., Lewis, and P. D., Welch. The fast transform algorithm: programming considerations in the calculation of sine, cosine and Laplace transforms. Proceedings of Cambridge Philosophical Society, 12:3 (1970), 315–337. (Cited on p. 234.)
[66] J. W., Cooley and J. W., Tukey. An algorithm for the machine computation of complex Fourier series. Mathematics of Computation, 19 (1965), 297–301. (Cited on p. 357.)
[67] R. B., Cooper. Introduction to Queueing Theory, 2nd edn (New York: North-Holland, 1981). (Cited on p. 731.)
[68] T. M., Cover and P. E., Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, IT-13:1 (1967), 21–27. (Cited on p. 621.)
[69] T. M., Cover and J. A., Thomas. Elements of Information Theory (New York: John Wiley & Sons, Inc., 1991). (Cited on p. 247.)
[70] D. R., Cox. Renewal Theory (Methuen, 1962). (Cited on p. 418.)
[71] D. R., Cox and P. A. W., Lewis. The Statistical Analysis of the Series of Events (London: Methuen, 1966). (Cited on pp. 145, 153, 418.)
[72] D. R., Cox and H. D., Miller. The Theory of Stochastic Processes (New York: John Wiley & Sons, Inc., 1965). (Cited on p. 516.)
[73] D. R., Cox and W. L., Smith. Queues (London: Methuen, 1961). (Cited on pp. 707, 731.)
[74] H., Cramér. Mathematical Methods of Statistics (Princeton, NJ: Princeton University Press, 1946). (Cited on pp. 305, 308, 532, 533, 536, 549.)
[75] K. S., Crump. Numerical inversion of Laplace transforms using a Fourier series approcimation. Journal of the ACM, 23:1 (1976), 89–96. (Cited on p. 234.)
[76] J. N., Daigle. Queueing Theory for Telecommunications (Reading, MA: Addison-Wesley, 1992). (Cited on p. 731.)
[77] W. B., Davenport, Jr. and W. L., Root. An Introduction to the Theory of Random Signals and Noise (New York: McGraw-Hill, 1958). (Cited on pp. 37, 394, 690.)
[78] F. N., David. Games, Gods and Gambling (London: Charles Griffin & Co., 1962). (Cited on pp. 7, 14.)
[79] G., Del Corso, A., Gulli, and F., Romani. Fast PageRank computation via a sparse linear system. Internet Mathematics, 2:3 (2005), 251–273. (Cited on pp. 384, 395.)
[80] A. P., Dempster, N. M., Laird, and D. B., Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B, 39:1 (1977), 1–38. (Cited on pp. 13, 559, 560, 563.)
[81] P. A. M., Dirac. Principles of Quantum Mechanics (Oxford, UK: Oxford University Press, 1935). (Cited on p. 46.)
[82] J. L., Doob. Stochastic Processes (New York: John Wiley & Sons, Inc., 1953). (Cited on pp. 268, 308, 323, 331, 347, 451, 492, 516, 690.)
[83] H., Dubner and J., Abate. Numerical inversion of Laplace transforms by relating them to the finite Fourier cosine transform. Journal of the ACM, 15:1 (1968), 115–123. (Cited on p. 234.)
[84] J., Dugundji. Envelope and pre-envelope of real waveforms. IRE Transactions on Information Theory, IT-4 (1958), 53–57. (Cited on p. 340.)
[85] R., Durbin, S. R., Eddy, A., Krogh, and G. J., Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids (Cambridge, UK: Cambridge University Press, 1998). (Cited on pp. 605, 615, 623.)
[86] A., Einstein. Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen (On the movement of small particles suspended in a stationary liquid demanded by the molecular-kinetic theory of heat). Annalen der Physik, 17 (1905), 549–560. (Cited on pp. 11, 499, 516.)
[87] A., Einstein. On the theory of Brownian motion. Annalen der Physik, 19 (1906), 371–381. (Cited on p. 516.)
[88] A., Einstein. Investigations of the Theory of the Brownian Movement (translated by A. D., Cowper) (New York: Dover, 1956). (Cited on p. 499.)
[89] E. O., Elliott. Estimates of error rates for codes on burst-noise channels. Bell System Technical Journal, 42 (1963), 1977–1997. (Cited on p. 582.)
[90] R., Ellis. Entropy, Large Deviations, and Statistical Mechanics (New York: Springer-Verlag, 2006). (Cited on p. 268.)
[91] T., Engset. Die Wahrscheinlichkeitsrechung zur Bestimmung der Wähleranzahl in automatischen Fernsprechämtern. Electrotechnische Zeitschrift, 31 (1918), 304–305. (Cited on p. 722.)
[92] Y., Ephraim. Statistical-model-based speech enhancement systems. Proceedings of the IEEE, 80:10 (1992), 1526–1555. (Cited on p. 605.)
[93] Y., Ephraim and N., Merhav. Hidden Markov processes. IEEE Transactions on Information Theory, 48:6 (2002), 1518–1569. (Cited on pp. 592, 606.)
[94] A. K., Erlang. The theory of probabilities and telephone conversations. Nyt Tidsskrift for Matematik B, 20 (1909), 33–39. (Cited on p. 695.)
[95] A. K., Erlang. Solution of some problems in the theory of probabilities of significance in automatic telephone exchanges. The Post Office Electrical Engineer's Journal, 10 (19171918), 189–197. (Cited on p. 6.)
[96] M., Feder and J., Catipovic. Algorithms for joint channel estimation and data recovery – application to equalization in underwater communications. IEEE Journal of Oceanic Engineering, 16 (1991), 42–55. (Cited on p. 566.)
[97] W., Feller. Über den zentralen Grenzwertsatz der Wahrscheinlichkeitsrechnung. Mathematische Zeitschrift, 40 (1935), 521–559. (Cited on p. 305.)
[98] W., Feller. A direct proof of Stirling's formula. The American Mathematical Monthly, 74:10 (1967), 1223–1225. (Cited on p. 275.)
[99] W., Feller. Introduction to Probability and Its Applications, Vol. I, 3rd edn (New York: John Wiley & Sons, Inc., 1968). (Cited on pp. 19, 34, 37, 38, 40, 59, 66, 77, 80, 82, 83, 84, 85, 104, 202, 235, 275, 302, 303, 308, 451, 486, 707.)
[100] W., Feller. Introduction to Probability and Its Applications, Vol. II, 2nd edn (New York: John Wiley & Sons, Inc., 1971). (Cited on pp. 202, 235, 251, 303, 304, 305, 308, 418, 451, 516, 518.)
[101] J., Felsenstein. Evolutionary trees from DNA sequences. Journal of Molecular Evolution, 17 (1981), 368–376. (Cited on p. 476.)
[102] J., Felsenstein. Inferring Phylogenies (Sunderland, MA: Sinauer Associates, 2004). (Cited on p. 477.)
[103] J. D., Ferguson. Variable duration models for speech. In Symposium on the Application of Hidden Markov Models to Text and Speech, pp. 143–179, Institute for Defense Analyses, Princeton, NJ, October 1980. (Cited on p. 606.)
[104] A. M., Ferrenberg, D. P., Landau, and Y. J., Wong. Monte Carlo simulations: hidden errors from “good” random number generators. Physical Review Letters, 69:23 (1992), 3382–3384. (Cited on p. 131.)
[105] T. L., Fine. Probability and Probabilistic Reasoning for Electrical Engineering (Upper Saddle River, NJ: Pearson Prentice Hall, 2006). (Cited on pp. 14, 37, 131, 549, 690.)
[106] R. A., Fisher. Design of Experiments, vol. 1, 3rd edn (Edinburgh: Oliver and Boyd, 1935). (Cited on p. 164.)
[107] G. D., Forney, Jr. Review of random tree codes. In Final Report on Contract NAS2-3637, NASA CR73176. NASA Ames Research Center, Ames, CA (December 1967). (Cited on p. 574.)
[108] G. D., Forney, Jr. The Viterbi algorithm (invited paper). Proceedings of the IEEE, IT-9:61 (1973), 268–278. (Cited on pp. 574, 591, 592, 605.)
[109] G. D., Forney, Jr. Maximum likelihood sequence estimation of digital sequences in the presence of intersymbol interference. IEEE Transactions on Information Theory, IT-18 (1972), 363–378. (Cited on pp. 592, 605.)
[110] D., Fox, W., Burgard, and S., Thrun. Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11 (1999), 391–427. (Cited on p. 615.)
[111] J., Franklin. The Science of Conjecture: Evidence and Probability before Pascal (Baltimore: The John Hopkins Press, 2001). (Cited on p. 14.)
[112] H., Freeman. Discrete-Time Systems (New York: John Wiley & Sons, Inc., 1965). (Cited on p. 211.)
[113] R. G., Gallager. Information Theory and Reliable Communications (New York: John Wiley & Sons, Inc., 1968). (Cited on p. 268.)
[114] D. P., Gaver. Diffusion approximation methods for certain congestion problems. Journal of Applied Probability, 5 (1968), 607–623. (Cited on p. 516.)
[115] D. P., Gaver, S. S., Lavenberg, and T. G., Price, Jr. Exploratory analysis of access path length data for a data base management system. IBM Journal of Research and Development, 20:5 (1976), 449–464. (Cited on p. 146.)
[116] E., Gelenbe and I., Mitrani. Analysis and Synthesis of Computer Systems (Academic Press, 1980). (Cited on p. 731.)
[117] A., Gelman, J. B., Carlin, H. S., Stern, and D. B., Rubin. Bayesian Data Analysis, 2nd edn (Boca Raton, FL: Chapman and Hall/CRC, 2003). (Cited on p. 104.)
[118] S., Geman and D., Geman. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:6 (1984), 721–741. (Cited on p. 639.)
[119] I. I., Gikhman and A. V., Skorokhod. Introduction to The Theory of Random Processes (W. B., Saunders Company, 1969). (Cited on p. 690.)
[120] E. N., Gilbert. Capacity of a burst-noise channel. Bell System Technical Journal, 39 (1960), 1253–1265. (Cited on p. 582.)
[121] W. R., Gilks, A., Thomas, and D. J., Spiegelhalter. A language and program for complex Bayesian modelling. The Statistician, 43 (1994), 169–178. (Cited on p. 641.)
[122] B. V., Gnedenko. Theory of Probability (New York: Chelsea, 1962). (Cited on p. 308.)
[123] B. V., Gnedenko and N., Kolmogorov. Limit Distributions for Sums of Independent Random Variables (Reading, MA: Addison Wesley, 1954). (Cited on p. 202.)
[124] G. H., Golub and C. F., Van Loan. Matrix Computations (Baltimore: The Johns Hopkins University Press, 1996). (Cited on pp. 361, 362, 370, 381, 395.)
[125] R. M., Gray. Toeplitz and Circulant Matrices: A Review (Norwell, MA: Now Publishers, 2006). (Cited on pp. 361, 362.)
[126] R. M., Gray and L., Davisson. An Introduction to Statistical Signal Processing (Cambridge University Press, 2004). (Cited on p. 37.)
[127] D., Green and J., Swets. Signal Detection Theory and Psychophysics (New York: John Wiley and Sons Inc., 1966). (Cited on p. 542.)
[128] E., Greenberg. Introduction to Bayesian Econometrics (Cambridge University Press, 2008). (Cited on pp. xxviii, 14, 643.)
[129] U., Grenander and G., Szego. Toeplitz Forms and Their Applications, 2nd edn (New York: Chelsea, 1984). (Cited on p. 361.)
[130] T. L., Grettenberg. A representation theorem for complex normal process. IEEE Transactions on Information Theory, IT-11 (1965), 395–306. (Cited on pp. 177, 332.)
[131] G. R., Grimmett and D. R., Stirzaker. Probability and Random Processes, (Oxford: Oxford University Press, 1992). (Cited on pp. 34, 37, 66, 104, 205, 235, 265, 271, 284, 287, 302, 303, 308, 310, 324, 325, 349, 418, 484, 516.)
[132] D., Gross, J. F., Shortle, J. M., Thompson, and C. M., Harris. Fundamentals of Queueing Theory, 4th edn (John Wiley & Sons, Inc., 2008). (Cited on p. 731.)
[133] J. A., Gubner. Probability and Random Processes for Electrical and Computer Engineers (Cambridge University Press, 2006). (Cited on pp. 37, 66, 104, 394, 516.)
[134] B., Gueye, A., Ziviani, M., Crovella, and S., Fdida. Constraint-based geolocation of Internet hosts. IEEE/ACM Transactions on Networking, 14:6 (2006), 1219–1232. (Cited on pp. 150, 151.)
[135] I., Hacking. The Emergence of Probability (New York: Cambridge University Press, 1975). (Cited on p. 14.)
[136] I., Hacking. An Introduction to Probability and Inductive Logic (New York: Cambridge University Press, 2001). (Cited on pp. 14, 37.)
[137] E., Haensler. Statische Signale: Grundlagen und Anwendungen. 3. Auflage (Berlin: Springer Verlag, 2001). (Cited on p. 690.)
[138] J., Hagenauer, E., Offer, and L., Parke. Iterative decoding of binary block and convolutional codes. IEEE Transactions on Information Theory, 42:2 (1996), 429–445. (Cited on p. 605.)
[139] A., Hald. Statistical Theory with Engineering Applications (New York: John Wiley & Sons, Inc., 1952). (Cited on pp. 142, 153, 549.)
[140] A., Hald. A History of Probability and Statistics, and Their Applications before 1750 (New York: Wiley, 1990 and 2003). (Cited on p. 14.)
[141] J. D., Hamilton. Time Series Analysis (Princeton, NJ: Princeton University Press, 1994). (Cited on pp. 322, 389, 395, 566.)
[142] J. D., Hamilton. Regime-switching models. In S., Durlauf and L., Blume, eds, New Palgrave Dictionary of Economics (Palgrave McMillan Ltd., 2008). (Cited on p. 5.)
[143] J. M., Hammersley and D. C., Handscomb. Monte Carlo Methods (London: Methuen, 1964). (Cited on p. 523.)
[144] E. J., Hannan. Time-Series Analysis (London: Methuen, 1960). (Cited on p. 357.)
[145] A. C., Harvey. Forecasting, Structural Time Series Models and the Kalman Filter (Cambridge University Press, 1989). (Cited on pp. 322, 389, 395.)
[146] B. R., Haverkort. Performance of Computer Communication Systems: A Model-Based Approach (New York: John Wiley & Sons, Inc., 1998). (Cited on p. 731.)
[147] J. F., Hayes and T. V. J., Ganesh Babu. Modeling and Analysis of Telecommunications Networks (Hoboken, NJ: John Wiley & Sons, Inc., 2004). (Cited on p. 731.)
[148] J. F., Hayes, T. M., Cover, and J. B., Riera. Optimal sequence detection and optimal symbolby-symbol detection: similar algorithms. IEEE Transactions on Commmunications, COM-30:1 (1982), 152–157. (Cited on p. 605.)
[149] S., Haykin. Neural Networks: A Comprehensive Foundation (Upper Saddle River, NJ: Prentice Hall, 1999). (Cited on pp. 3, 616.)
[150] C. W., Helstrom. Statistical Theory of Signal Detection (Pergamon Press, 1960). (Cited on pp. 177, 340, 542, 549.)
[151] D. P., Heyman and S., Stidham, Jr. The relation between customers and time averages in queues. Operations Research, 28 (1980), 983–994. (Cited on p. 730.)
[152] W., Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58:1 (1963), 13–30. (Cited on p. 272.)
[153] K., Hoffman and R., Kunze. Linear Algebra (Englewood Cliffs, NJ: Prentice Hall, 1961). (Cited on p. 241.)
[154] J. J., Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, USA, 79 (1982), 2554–2558. (Cited on p. 616.)
[155] J. Y., Hui. Switching and Traffic Theory for Integrated Broadband Networks (Boston, MA: Kluwer, 1990). (Cited on pp. 268, 737.)
[156] Ibm, . IBM Subroutine Library – Mathematics, User's Guide, SH12-5300-1, 2nd edn (IBM Corporation, 1974). (Cited on p. 234.)
[157] K., Itô. Stochastic integral. Proceedings of the Imperial Academy (Tokyo), 20 (1944), 519–524. (Cited on p. 11.)
[158] K., Itô. Selected Papers (Edited by D. W., Stroock and S. R., Sriniva Varadhan) (New York: Springer, 1987). (Cited on p. 11.)
[159] K., Itô and J. H. P., McKean. Diffusion Processes and Their Sample Paths (Berlin: Springer, 1965). (Cited on pp. 506, 516.)
[160] D. L., Jagerman. An inversion technique for the Laplace transform with applications. Bell System Technical Journal, 57 (1978), 669–710. (Cited on p. 234.)
[161] D. L., Jagerman. An inversion technique for the Laplace transform. Bell System Technical Journal, 61 (1982), 1995–2002. (Cited on p. 234.)
[162] D. L., Jagerman, B., Melamed, and W., Willinger. Stochastic modeling of traffic processes. In J. H., Dshalalow, ed., Frontiers in Queueing: Models and Applications in Science and Engineering pp. 271–320. (Boca Raton, FL: CRC Press, 1997). (Cited on pp. 393, 395.)
[163] F., Jelinek. Continuous speech recogniton by statistical methods. Proceedings of the IEEE, 64 (1976), 532–556. (Cited on p. 605.)
[164] F., Jelinek. Statistical Methods for Speech Recognition (MIT Press, 1998). (Cited on pp. 425, 605, 615.)
[165] F., Jelinek, L., Bahl, and R., Mercer. Design of a linguistic statistical decoder for the recognition of continuous speech. IEEE Transactions on Information Theory, 21 (1975), 250–256. (Cited on p. 605.)
[166] F. V., Jensen and T. D., Nielsen. Bayesian Networks and Decision Graphs, Information Science and Statistics Series, 2nd edn (New York: Springer-Verlag, 2007). (Cited on p. 624.)
[167] M. C., Jeruchim, P., Balaban, and K. S., Shanmugan. Simulation of Communication Systems: Modeling, Methodology, and Techniques (Kluwer Academic/Plenum Publishers, 2000). (Cited on p. 268.)
[168] W. S., Jewell. A simple proof of L = λW. Operations Research, 15:6 (1967), 1109–1116. (Cited on p. 730.)
[169] I. T., Jolliffe. Principal Component Analysis, 2nd edn (New York: Springer, 2002). (Cited on p. 395.)
[170] T., Kailath, A., Sayed, and B., Hassibi. Linear Prediction (Prentice-Hall, 2000). (Cited on p. 690.)
[171] R., Kalman. A new approach to linear filtering and predicition problems. Journal of Basic Engineering, 82 (1960), 35–45. (Cited on pp. 13, 645.)
[172] R., Kalman and R. S., Bucy. New results in linear filtering and predicition theory. Journal of Basic Engineering, 83 (1961), 95–107. (Cited on p. 645.)
[173] E. P. C., Kao. An Introduction to Stochastic Processes (Belmont, CA: Duxbury Press, 1979). (Cited on pp. 418, 512.)
[174] K., Karhunen. Über linearen Methoden in der Wahrscheinlichkeitsrechnung. Annales Academiae Scientarum Fennicae, Series A 1, Mathematica–Physica, 37 (1947), 3–79. (Cited on p. 365.)
[175] S., Karlin and H. M., Taylor. A First Course in Stochastic Processes, 2nd edn (Academic Press, 1975). (Cited on pp. 324, 325, 340, 341, 418, 443, 690.)
[176] J., Kay. The EM algorithm in medical imaging. Statistical Methods in Medical Research, 6:1 (1975), 55–75. (Cited on p. 566.)
[177] F. P., Kelly. Reversibility and Stochastic Networks (John Wiley & Sons, Inc., 1979). (Cited on pp. 477, 704, 719, 731.)
[178] F. P., Kelly. Loss networks (invited paper). The Annals of Applied Probability, 1 (1991), 319–378. (Cited on p. 724.)
[179] M. G., Kendall and A., Stuart. The Advanced Theory of Statistics, Vol. II: Inference and Relationship (London: Charles Griffin, 1961). (Cited on p. 549.)
[180] M., Kijima. Markov Processes for Stochastic Modeling (Chapman & Hall, 1997). (Cited on p. 477.)
[181] F. W., King. Hilbert Transforms, Vol. 1 (Cambridge University Press, 2009). (Cited on p. 340.)
[182] F. W., King. Hilbert Transforms, Vol. 2 (Cambridge University Press, 2010). (Cited on p. 340.)
[183] P. J. B., King. Computer and Communication System Performance Modeling (Englewood Cliffs, NJ: Prentice Hall, 1990). (Cited on p. 731.)
[184] J. F. C., Kingman. A martingale inequality in the theory of queues. Proceedings of the Cambridge Philosophical Society, 59 (1964), 359–361. (Cited on p. 273.)
[185] J. F. C., Kingman. Inequalities in the theory of queues. Journal of Royal Statistical Society, B 32 (1970), 102–110. (Cited on p. 273.)
[186] J. F. C., Kingman. Poisson Processes (Clarendon Press, 1992). (Cited on p. 10.)
[187] J., Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46:5 (1999), 604–632. (Cited on pp. 384, 395.)
[188] L., Kleinrock. Time-shared systems: a theoretical treatment. Journal of the ACM, 14 (1967), 242–261. (Cited on p. 716.)
[189] L., Kleinrock. Queueing Systems, Vol. I: Theory (New York: JohnWiley & Sons, Inc., 1975). (Cited on pp. 235, 419, 451, 731.)
[190] L., Kleinrock. Queueing Systems, Vol. II: Computer Applications (New York: John Wiley & Sons, Inc., 1976). (Cited on pp. 235, 724.)
[191] D. E., Knuth. The Art of Computer Programming: Vol. 2. Seminumerical Algorithms, 3rd edn (Upper Saddle River, NJ: Addison Wesley, 1998). (Cited on pp. 126, 130, 131.)
[192] H., Kobayashi. Representation of complex-valued vector processes and their application to estimation and detection. Ph.D. Thesis, Princeton University, August 1967. (Cited on pp. 177, 340.)
[193] H., Kobayashi. A simultaneous adaptive estimation and decision algorithm for carrier modulated data transmission systems. IEEE Transactions on Communication Technology, COM-19:3 (1971), 268–279. (Cited on pp. 322, 566.)
[194] H., Kobayashi. Application of the diffusion approximation to queueing networks I: equilibrium queue distributions. Journal of the ACM, 21:2 (1974), 316–328. (Cited on p. 516.)
[195] H., Kobayashi. Application of the diffusion approximation to queueing networks II: nonequilibrium distributions and applications to computer modeling. Journal of the ACM, 21:3 (1974), 459–469. (Cited on p. 516.)
[196] H., Kobayashi. Bounds for the waiting time in queueing systems. In E., Gelenbe and R., Mahl, eds, Computing Architectures and Networks (Amsterdam: North-Holland Publishing Company, February 1974) pp. 163–274. (Cited on p. 273, 432.)
[197] H., Kobayashi. Modeling and Analysis: An Introduction to System Performance Evaluation Methodology (Reading, MA: Addison-Wesley, 1978). (Cited on pp. 153, 235, 419, 653, 656, 708, 731.)
[198] H., Kobayashi. Partial-response coding, maximum-likelihood decoding: capitalizing on the analogy between communication and recording. IEEE Communications Magazine, 47:3 (2009), 14–17. (Cited on pp. 592, 605, 607.)
[199] H., Kobayashi. Application of probabilistic decoding to digital magnetic recording systems. IBM Journal of Research and Development, 15:1 (1971), 69–74. (Cited on pp. 592, 605, 609.)
[200] H., Kobayashi. Correlative level coding and maximum likelihood decoding. IEEE Transactions on Information Theory, IT-17:5 (1971), 586–594. (Cited on pp. 592, 605, 609.)
[201] H., Kobayashi and B. L., Mark. Product-form loss networks. In J. H., Dshalalow, ed., Frontiers in Queueing: Models and Applications in Science and Engineering (New York: CRC Press, 1997) pp. 147–196. (Cited on pp. 727, 729, 731.)
[202] H., Kobayashi and B. L., Mark. Generalized loss models and queueing-loss networks. International Transactions on Operational Research, 9:1 (2002), 97–112. (Cited on p. 731.)
[203] H., Kobayashi and B. L., Mark. System Modeling and Analysis: Foundations for System Performance Evaluation (Prentice Hall, 2009). (Cited on pp. xxviii, 123, 124, 125, 126, 130, 235, 268, 350, 419, 451, 477, 480, 516, 632, 634, 635, 707, 708, 719, 721, 722, 724, 725, 726, 727, 728, 729, 731, 732, 733, 735, 737, 738, 739.)
[204] H., Kobayashi and D. T., Tang. Application of partial-response channel coding to magnetic recording systems. IBM Journal of Research and Development, 14:4 (1970), 368–75. (Cited on p. 607.)
[205] P., Koehn. Statistical Machine Translation (Cambridge University Press, 2010). (Cited on p. 566.)
[206] D., Koller and N., Friedman. Probabilistic Graphical Models (Cambridge, MA: MIT Press, 2009). (Cited on pp. 3, 14, 643.)
[207] A. N., Kolmogorov. Sur la loi forte des grands nombres. Comptes Rendus des Séances de l'Académie des Sciences, 191 (1930), 910–912. (Cited on p. 302.)
[208] A. N., Kolmogorov. Grundbegriffe der Wahrscheinlichkeitsrechnung (Berlin: Julius Springer, 1933). (Cited on pp. 9, 20.)
[209] A. N., Kolmogorov. Interpolation and extrapolation. Bulletin de l'Academie des Sciences de U.S.S.R., Series Mathematics, 5 (1941), 3–14. (Cited on pp. 13, 656.)
[210] A. N., Kolmogorov. Foundations of the Theory of Probability (translated by Nathaniel Morrison) (New York: Chelsea, 1950). (Cited on p. 9.)
[211] I., Kononenko and M., Kukar. Machine Learning and Data Mining: Introduction to Principles and Algorithms (Chichester: Horwood Publishing, Ltd, 2007). (Cited on pp. xxviii, 14, 643.)
[212] V., Kotelnikov. On the capacity of ‘ether’ and cables in electrical communications (in Russian). In Proceedings of the First All-Union Conference on Questions of Communications, Moscow, 1933. (Cited on p. 352.)
[213] F. R., Kschischang, B. J., Frey, and H. A., Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47:2 (2001), 498–519. (Cited on pp. 606, 643.)
[214] E. R., Ktretzmer. Generalization of a technique for binary data transmission. IEEE Transactions on Communications Technology, COM-14 (1966), 67–68. (Cited on p. 607.)
[215] S., Kullback. Information Theory and Statistics (New York, NY: John Wiley & Sons, Inc., 1959). (Cited on p. 557.)
[216] A., Kumar and L., Cowen. Augmented training of hidden Markov models to recognize remote homologs via simulated evolution. Bioinformatics, 25:13 (2009), 1602–1608. (Cited on p. 605.)
[217] S. Y., Kung, K. S., Arun, and D. V. B., Rao. State space and singular value decomposition based approximation methods for harmonic retrieval. Journal of the Optical Society of America, 73:12 (1983), 1799–1811. (Cited on p. 395.)
[218] A., Langville and C., Meyer. A survey of eigenvector methods forWeb information retrieval. SIAM Review, 47:1 (2005), 135–161. (Cited on pp. 384, 395, 451.)
[219] S. S., Lavenberg, ed. Computer Performance Modeling Handbook (Orlando, FL: Academic Press, 1983). (Cited on p. 731.)
[220] S. S., Lavenberg and M., Reiser. Stationary state probabilities of arrival instants for closed queueing networks with multiple types of customers. Journal of Applied Probability, 17 (1980), 1048–1061. (Cited on p. 710.)
[221] E. L., Lehmann. Testing Statistical Hypotheses (New York: Springer, 1986). (Cited on p. 549.)
[222] A., Leon-Garcia. Probability and Random Processes for Electrical Engineering, 2nd edn (Reading, MA: Addison-Wesley, 1994). (Cited on pp. 37, 549.)
[223] D. A., Levin, Y., Peres, and E. L., Wilmer. Markov Chains and Mixing Times (American Mathematical Society, 2008). (Cited on p. 635.)
[224] S., Levinson. Continuously variable duration hidden Markov models for automatic speech recognition. Computer Speech and Langauge, 1:1 (1986), 29–45. (Cited on p. 606.)
[225] P. A. W., Lewis and G. S., Shedler. Statistical analysis of non-stationary series of events in a data base system. IBM Journal of Research and Development, 20:5 (1976), 429–528. (Cited on p. 146.)
[226] J. W., Lindeberg. Eine neue Herleitung des Exponentialgesetzes in der Wahrscheinlichkeitsrechnung. Mathematische Zeitschrift, 15 (1922), 211–225. (Cited on p. 305.)
[227] L., Lipsky. Queueing Theory: A Linear Algebraic Approach (New York: MacMillan, 1992). (Cited on p. 731.)
[228] J. D. C., Little. A proof of the queueing formula L = λW. Operations Research, 9 (1961), 383–387. (Cited on p. 696.)
[229] M., Loève. Sur les fonctions aléatoires du second ordre. Revue Scientifique, 83 (1945), 297–303. (Cited on p. 365.)
[230] M., Loève. Probability Theory (Princeton, NJ: D. Van Nostrand, 1955). (Cited on p. 308.)
[231] A., Lyapunov. Sur une proposition de la théorie des probabilités. Bulletin de l'Academie Impériale des Sciences de St. Petersbourg, 13 (1900), 359–386. (Cited on p. 304.)
[232] A., Lyapunov. Nouvelle forme de la théoreme dur la limite de probabilité. Mémoires de l'Academie Impériale des Sciences de St. Petersbourg, 12 (1901), 1–24. (Cited on p. 304.)
[233] X., Ma, H., Kobayashi, and S. C., Schwartz. EM-based channel estimation algorithms for OFDM. EURASIP Journal on Applied Signal Processing, 10 (2004), 1460–1477. (Cited on p. 566.)
[234] D. J. C., MacKay. Information Theory, Inference, and Learning Algorithms (Cambridge University Press, 2003). (Cited on pp. 14, 643.)
[235] L. E., Maistrov. Probability Theory: A Historical Sketch (New York: Academic Press, 1974). (Cited on p. 14.)
[236] B. B., Mandelbrot and J. V., Ness. Fractional Brownian motions, fractional noise and applications. SIAM Review, 10 (1968), 422–437. (Cited on p. 516.)
[237] A. A., Markov. Rasprostranenie zakona bol'shih chisel na velichiny, zavisyaschie drug ot druga. Izvestiya Fiziko-matematicheskogo obschestva pri Kazanskom universitete, 2-ya seriya, 15 (1906), 135–156. (Cited on pp. 318, 319.)
[238] A. A., Markov. Investigations of an important case of dependent trials (in Russian). Izvestiya Academii, Nauk, Series 6 (St. Petersburg), 1:3 (1907), 61–80. (Cited on pp. 3, 9, 10.)
[239] A. A., Markov. Ob ispytaniyah, svyazannyh v cep ne nablyudaemymi sobytiyami (on trials associated into a chain by unobserved events). Izvestiya Akademii Nauk, SPb (News of the Academy of Sciences, St. Petersburg), VI seriya 6:98 (1912), 551–572. (Cited on pp. 319, 478, 605.) 752
[240] A. A., Markov. Extension of the limit theorems of probability theory to a sum of variables connected in a chain (translated by S. Petelin). In R. A., Howard, ed., Dynamic Probabilities Systems, Vol. 1 (New York: Wiley, 1971) pp. 552–576. (Cited on pp. 319, 478.)
[241] W. N., Martin and W. M., Spears, eds. Foundations of Genetic Algorithms (Morgan and Kaufmann/Academic Press, 2001). (Cited on p. 616.)
[242] L., Mason, J., Baxter, P., Bartlett, and M., Frean. Boosting algorithms as gradient descent. In S. A., Solla, T. K., Leen, and K.-R., Muller, eds, Advances in Neural Information Processing Systems (MIT Press, 2000) pp. 512–518. (Cited on p. 616.)
[243] J. H., Matthews and R. W., Howell. Complex Analysis for Mathematics and Engineering (Jones & Bartlett Publishers, Inc., 2006). (Cited on p. 195.)
[244] R. J., McEliece and S. M., Aji. The generalized distributive law. IEEE Transactions on Information Theory, 46:2 (2000), 325–343. (Cited on p. 631.)
[245] R. J., McEliece, D. J. C., MacKay, and J. F., Cheng. Turbo decoding as an instance of Pearl's ‘belief propagation’ algorithm. IEEE Journal on Selected Areas in Communications, 16:2 (1998), 140–152. (Cited on pp. 606, 624.)
[246] G., McLachlan and T., Krishnan. The EM Algorithm and Exensions (John Wiley & Sons, 1997). (Cited on pp. 563, 564, 566.)
[247] N., Metropolis, A. W., Rosenbluth, M. N., Rosenbluth, A. H., Teller, and E., Teller. Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21:6 (1953), 1087–1092. (Cited on p. 636.)
[248] D., Middleton. An Introduction to Statistical Communication Theory (New York: McGraw- Hill, 1960). (Cited on p. 549.)
[249] N. M., Mirasol. The output of an M/G/∞ queue is Poisson. Operations Research, 11 (1963), 282–284. (Cited on p. 733.)
[250] C., Mitchell, M., Harper, and L., Jamieson. On the complexity of explicit duration HMMs. IEEE Transactions on Speech and Audio Processing, 3:2 (1995), 213–217. (Cited on p. 606.)
[251] P. M., Morse. Queues, Inventries and Maintenance (New York:Wiley & Sons, 1958). (Cited on p. 731.)
[252] M. E., Munroe. Introduction to Measure and Integration (Reading, MA: Addison-Wesley, 1953). (Cited on p. 293.)
[253] L. B., Nelson and H. V., Poor. Iterative multiuser receivers for CDMA channels: an EMbased approach. IEEE Transactions on Communications, 44 (1996), 1700–1710. (Cited on p. 566.)
[254] R., Nelson. Probability, Stochastic Processes, and Queueing Theory (New York: Springer-Verlag, 1995). (Cited on pp. 37, 66, 104, 275, 418, 419, 451, 731.)
[255] G. F., Newell. Applications of Queueing Theory (London: Chapman & Hall, 1971). (Cited on p. 516.)
[256] J., Neyman and E., Pearson. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 231 (1933), 289–337. (Cited on pp. 539, 540.)
[257] H., Nyquist. Certain topics in telegraph transmission theory. Transactions of the AIEE, 47 (1928), 363–390. (Cited on p. 353.)
[258] K., Ogura. On a certain transcendental integral function in the theory of interpolation. Tohoku Mathematical Journal, 17 (1920), 64–72. (Cited on p. 352.)
[259] J. K., Omura. On the Viterbi decoding algorithm. IEEE Transactions on Information Theory, IT-15 (1969), 77–179. (Cited on p. 592.)
[260] J. K., Omura. Optimal receiver design for convolutional codes and channels with memory via control theoretic concepts. Information Science, 3 (1971), 243–266. (Cited on p. 592.)
[261] M. F. M., Osborne. Brownian motion in the stock market. Operations Research, 7:2 (1959), 145–173. (Cited on p. 5.)
[262] A., Papoulis and U., Pillai. Probability, Random Variables, and Stochastic Processes, 4th edn (New York: McGraw-Hill, 2002). (Cited on pp. 37, 66, 104, 108, 131, 325, 394, 549, 690.)
[263] E., Parzen. Stochastic Processes (San Francisco: Holden-Day, Inc., 1962). (Cited on p. 323.)
[264] J., Pearl. Bayesian networks: A model of self-activated memory for evidential reasoning. In UCLA Computer Science Department Technical Report 850021; Proceedings, Cognitive Science Society, pp. 329–334, UC Irvine, August 1985. (Cited on p. 477.)
[265] J., Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (San Mateo, CA: Morgan Kaufmann Publishers, 1988). (Cited on p. 643.)
[266] K., Pearson. On a criterion that a system of deviations from the probable in the case of a correlated system of variables in such that it can be reasonably supposed to have arisen in random sampling. Philosophical Magazine, 50 (1900), 157–175. (Cited on p. 157.)
[267] K., Pearson. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 6:2 (1901), 559–572. (Cited on p. 372.)
[268] M., Pepe. The Statistical Evaluation of Medical Tests for Classification and Prediction (New York: Oxford University Press, 2003). (Cited on p. 542.)
[269] W. W., Peterson, T. G., Birdsall, and W. C., Fox. The theory of signal detectability. Transactions of I.R.E., PGIT-4 (1954), 171–212. (Cited on p. 542.)
[270] J., Piasecki. Centenary of Marian Smoluchowski. Acta Physica Polonica B, 38:5 (2007), 1623–1629. (Cited on p. 11.)
[271] H. V., Poor. An Introduction to Signal Detection and Estimation (Springer, 1994). (Cited on p. 549.)
[272] H. V., Poor. Sequence detection: backward and forward in time. In R. E., Blahut and R., Koetter, eds, Codes, Graphs and Systems (Boston, MA: Kluwer, 2002) pp. 93–112. (Cited on pp. 592, 605.)
[273] H. V., Poor. Dynamic programming in digital communications: Viterbi decoding to turbo multiuser detection. Journal of Optimization Theory and Applications, 115:3 (2002), 629–657. (Cited on pp. 592, 605.)
[274] L. R., Rabiner. A tutorial on hidden Markov models and selected application in speech recognition. Proceedings of the IEEE, 77:2 (1989), 257–286. (Cited on p. 605.)
[275] L. R., Rabiner and B. H., Juang. Fundamentals of Speech Recogntion (Prentice Hall, 1993). (Cited on pp. 605, 615.)
[276] L. R., Rabiner, S. E., Levinson, and M. M., Sondhi. On the application of vector quantization and hidden Markov models to speaker-independent, isolated word recogition. Bell System Technical Journal, 62:4 (1983), 1075–1105. (Cited on p. 605.)
[277] C. R., Rao. Information and the accuracy attainable in the estimation of statistical parameters. Bulletin of the Calcutta Mathematical Society, 37 (1945), 81–91. (Cited on p. 532.)
[278] C. R., Rao. Linear Statistical Inference and Its Applications (New York: JohnWiley & Sons, Inc., 1965). (Cited on pp. 270, 302, 303, 304, 305, 308, 549, 651.)
[279] H. E., Rauch, F., Tung, and C. T., Striebel. Maximum likelihood estimates of linear dynamic systems. AIAA Journal, 3:8 (1965), 1445–1450. (Cited on pp. 606, 690.)
[280] S. O., Rice. Statistical properties of a sine wave plus random noise. Bell System Technical Journal, 27 (1948), 109–157. (Cited on p. 170.)
[281] W., Roberts, Y., Ephraim, and E., Dieguez. On Rydén's EM algorithm for estimating MMPPs. IEEE Signal Processing Letters, 13:6 (2006), 373–376. (Cited on pp. 566, 606.)
[282] L. C. G., Rogers and D., Williams. Diffusions, Markov Processes and Martingales: Volume 1, Foundations (Cambridge University Press, 2000). (Cited on pp. 268, 477, 516.)
[283] L. C. G., Rogers and D., Williams. Diffusions, Markov Processes and Martingales: Volume 2, Itô Calculus (Cambridge University Press, 2000). (Cited on p. 516.)
[284] V., Romanovsky. Diskretnye tsepi Markova (Moscow: Gostekhizdat, 1949). (Cited on p. 605.)
[285] V., Romanovsky. Discrete Markov Chain (translated by E., Senata) (Groningen: Wolters-Noordhoff, 1970). (Cited on p. 605.)
[286] M., Rosenblatt. Random Processes (New York: Oxford University Press, 1962). (Cited on p. 451.)
[287] S. M., Ross. Stochastic Processes (New York: John Wiley & Sons, Inc., 1983). (Cited on p. 719.)
[288] S. M., Ross. Stochastic Processes, 2nd edn (New York: John Wiley & Sons, Inc., 1996). (Cited on pp. 272, 273, 418, 451, 516.)
[289] S. M., Ross. A First Course in Probability, 6th edn (Prentice Hall, 2002). (Cited on pp. 37, 66, 104, 268, 271.)
[290] H. L., Royden. Real Analysis (Prentice Hall, 1988). (Cited on p. 293.)
[291] T., Rydén. An EM algorithm for estimation in Markov-modulated Poisson process. Communications in Statististical Data Analysis, 21 (1996), 431–447. (Cited on pp. 566, 606.)
[292] M., Sakata, S., Noguchi, and J., Oizumi. Analysis of a processor-sharing queueing model for time-sharing systems. In Proceedings of 2nd Hawaii International Conference on System Science, pp. 625–628 (1969). (Cited on p. 717.)
[293] R., Schapire. Strength of weak learnability. Machine Learning, 5:2 (1990), 197–227. (Cited on p. 616.)
[294] A., Schuster. On lunar and solar periodities of earthquakes. Proceedings of the Royal Society, 61 (1897), 455–465. (Cited on p. 357.)
[295] M., Schwartz, W. R., Bennet, and S., Stein. Communication Systems and Techniques (New York: Wiley, 1995). (Cited on pp. 336, 340.)
[296] C., Semple and M., Steel. Phylogenetics, volume 24 of Oxford Lecture Series inMathematics and Its Applications (Oxford University Press, 2003). (Cited on pp. 473, 477.)
[297] K., Sevcik and I., Mitrani. The distribution of queueing network states at input and output instants. Journal of the ACM, 28:2 (1981), 358–371. (Cited on p. 710.)
[298] G., Shafer. The Art of Causal Conjecture (MIT Press, 1996). (Cited on p. 477.)
[299] G., Shafer and V., Vovk. Probability and Finance: It's Only a Game! (John Wiley & Sons, 2001). (Cited on pp. xxviii, 14, 268, 304, 308.)
[300] C. E., Shannon. A mathematical theory of communications. Bell System Technical Journal, 27 (1948), 379–423, 623–656. (Cited on pp. 3, 246, 256, 257, 352, 425, 427, 451, 561, 568, 581, 605.)
[301] C. E., Shannon. Communication in the presence of noise. Proceedings of the Institute of Radio Engineers, 37:1 (1949), 10–21. (Also in Proceedings of the IEEE, 86:2 (1998), 447–457.) (Cited on p. 352, 353.)
[302] W. T., Shaw. Complex Analysis with Mathematica (Cambridge University Press, 2006). (Cited on p. 195.)
[303] L. A., Shepp and Y., Vardi. Maximum likelihood reconstruction for emission tomography. IEEE Medical Imaging, MI-1:2 (1983), 113–122. (Cited on p. 566.)
[304] A., Shwartz and A., Weiss. Large Deviations for Performance Analysis (Chapman & Hall, 1995). (Cited on p. 268.)
[305] C. A., Sims. Macroeconomics and reality. Econometrica, 48:1 (1980), 1–48. (Cited on p. 5.)
[306] D., Skillicorn. Understanding Complex Datasets: Data Mining with Matrix Decomposition (Chapman & Hall/CRC, 2007). (Cited on p. 395.)
[307] M., Smoluchowski. Essai d'une théorie cinétique du mouvement Brownien et des milieux troubles (Outline of the kinetic theory of Brownian motion of suspensions). Bulletin International de l'Académie des Sciences de Cracovie, (1906), 577–602. (Cited on p. 11.)
[308] I., Someya. Waveform Transmission (in Japanese) (Tokyo: Shukyosha, 1949). (Cited on p. 352.)
[309] D. J., Spiegelhalter, R., Franklin, and K., Bull. Assessment, criticism, and improvement of imprecise probabilities for a medical expert system. In Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence, pp. 285–294 (1989). (Cited on pp. 615, 624.)
[310] H., Stark and J. W., Woods. Probability and Random Processes with Applications to Signal Processing, 3rd edn (Upper Saddle River, NJ: Prentice Hall, 2002). (Cited on pp. 37, 566, 690.)
[311] S., Stidham, Jr. and M., El-Taha. Sample-path techniques in queueing theory. In J. H., Dshalalow, ed., Advances in Queueing: Theory, Methods, and Open Problems (CRC Press, 1995), pp. 119–166. (Cited on p. 730.)
[312] S. M., Stigler. The History of Statistics: The Measurement of Uncertainty before 1900 (Cambridge, MA: Harvard University Press, 1986). (Cited on p. 14.)
[313] R. L., Stratonovich. Application of the Markov process theory to optimal filtering. Radio Engineering and Electronic Physics, 5:11 (1960), 1–19. (Cited on p. 12.)
[314] (W. S., Gosset). The probable error of a mean. Biometrika, 6:1 (1908), 1–25. (Cited on p. 162.)
[315] A. L., Sweet and J. C., Hardin. Solutions for some diffusion processes with two barriers. Journal of Applied Probability, 7 (1970), 423–431. (Cited on p. 518.)
[316] R., Syski. Introduction to Congestion Theory in Telephone Systems, 2nd edn (Amsterdam: North-Holland, 1986). (Cited on pp. 720, 731.)
[317] L., Takács. Introduction to the Theory of Queues (New York: Oxford University Press, 1962). (Cited on p. 731.)
[318] S., Tezuka. Uniform Random Numbers: Theory and Practice (Norwell, MA: Kluwer Academic Publishers, 1995). (Cited on p. 131.)
[319] J. B., Thomas. An Introduction to Applied Probability and Random Processes (New York: John Wiley & Sons, Inc., 1971). (Cited on pp. 37, 131, 278, 302, 303, 304, 308, 309, 311, 323, 394, 690.)
[320] L., Tierney. Markov chains for exploring posterior distributions. Annals of Statistics, 22:4 (1994), 1701–1728. (Cited on p. 637.)
[321] L., Tierney. A note on Metropolis–Hastings kernels for general state spaces. Annals of Applied Probability, 8:1 (1998), 1–9. (Cited on p. 637.)
[322] H. C., Tijms. Stochastic Modeling and Analysis (John Wiley & Sons, Inc., 1986). (Cited on p. 731.)
[323] E. C., Titchmarsh. Theory of Functions (London: Oxford University Press, 1939). (Cited on p. 194.)
[324] E. C., Titchmarsh. Introduction to the Theory of Fourier Integrals (London: Oxford University Press, 1948). (Cited on p. 345.)
[325] I., Todhunter. A History of the Mathematical Theory of Probability from the Time of Pascal to that of Laplace (New York: Chelsea, 1949, 1965). (Originally published by Macmillan in 1865.) (Cited on p. 14.)
[326] ,Tree of Life Web Project. WWW page, August 2010. (Cited on p. 470.)
[327] K. S., Trivedi. Probability & Statistics with Reliability, Queueing and Computer Science Applications, 2nd edn (New York: John Wiley & Sons, Inc., 2002). (Cited on pp. 37, 418, 419.)
[328] J. W., Tukey. Exploratory Data Analysis (Reading, MA: Addison-Wesley, 1977). (Cited on p. 153.)
[329] G. L., Turin. On optimal diversity reception. IRE Transactions on Information Theory, IT-7 (1961), 154–167. (Cited on pp. 177, 322.)
[330] W., Turin. Digital Transmission Systems: Performance Analysis and Modeling (McGraw Hill, 1999). (Cited on p. 432.)
[331] W., Turin. MAP decoding in channels with memory. IEEE Transactions on Communications, 48:5 (2000), 757–763. (Cited on p. 566.)
[332] W., Turin. Performance Analysis and Modeling of Digital Transmission Systems (Kluwer Academic/Plenum Pulishers, 2004). (Cited on pp. 456, 566, 606.)
[333] W., Turin and R., Boie. Bar code recovery via the EM algorithm. IEEE Transactions on Signal Processing, 46:2 (1998), 354–363. (Cited on p. 566.)
[334] G. E., Uhlenbeck and L. S., Ornstein. On the theory of Brownian motion. Physical Review, 36 (1930), 823–841. (Cited on pp. 502, 516.)
[335] P., Valkó. Numerical inversion of Laplace transform: a challenge for developers of numerical methods.∼valko/Nil/ (2003). (Cited on pp. 233, 234.)
[336] A. J., van der Veen. Algebraic method for deterministic blind beamforming. Proceedings of IEEE, 86:10 (1998), 1987–2008. (Cited on p. 395.)
[337] V., Vapnik. The Nature of Statistical Learning Theory (New York: Springer-Verlag, 1995). (Cited on p. 643.)
[338] V., Vapnik. Statistical Learning Theory (New York: John Wiley, 1998). (Cited on pp. 3, 616, 643.)
[339] A. J., Viterbi. Error bounds for convolutional codes and asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, IT-13 (1967), 260–269. (Cited on pp. 591, 592, 605.)
[340] A. J., Viterbi. A personal history of the Viterbi algorithm. IEEE Signal Processing Magazine, 23:4 (2006), 120–142. (Cited on p. 605.)
[341] R., von Mises. Wahrscheinlichkeitsrechnung, Statistik und Wahrheit (Wien: Verlang von Julius Springer, 1928). (Cited on p. 19.)
[342] R., von Mises. Probability, Statistics and Truth (New York: MacMillan, 1954) (Translation of the 1928 publication.) (Cited on p. 9.)
[343] L. A., Wainstein and V. D., Zubakov. Extraction of Signals from Noise (Prentice-Hall Inc, 1962). (Cited on p. 177.)
[344] M. E., Wall, A., Rechtsteiner, and L. M., Rocha. Singular value decomposition and principal component analysis. In D. P., Berrar, W., Dubitzky, and M., Granzow, eds, A Practical Approach to Microarray Data Analysis (Norwell, MA: Kluwer Academic Press, 2003) pp. 91–109. (Cited on pp. 372, 395.)
[345] J., Walrand. An Introduction to Queueing Networks (Englewood Cliffs, NJ: Prentice Hall, 1988). (Cited on p. 479.)
[346] L. R., Welch. The shannon lecture: Hidden Markov models and the Baum–Welch algorithms. IEEE Informathion Theory Society Newsletter, 53:4 (2003), 1, 10–13. (Cited on pp. 598, 606.)
[347] P. D., Welch. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on Audio Electronics, AU-15 (1967), 70–73. (Cited on p. 357.)
[348] E. T., Whittaker. On the functions which are represented by the expansions of the interpolation theory. Proceedings of the Royal Society of Edinburgh, Section A, 35 (1915), 181–194. (Cited on p. 352.)
[349] N., Wiener. The Fourier Integral and Certain of Its Applications (Cambridge University Press, 1933). (Reissued in 1988.) (Cited on p. 194.)
[350] N., Wiener. Extrapolation, Interpolation, and Smoothing of Stationary Time Series (New York: John Wiley, 1949). (Cited on pp. 13, 645, 656, 690.)
[351] N., Wiener. Collected Works, Vol. I (Edited by P. R. Masani) (Cambridge, MA: MIT Press, 1976). (Cited on p. 11.)
[352] D. J., Wilkinson. Bayesian methods in bioinformatics and computational systems biology. Briefings in Bioinformatics, 8:2 (2007), 109–116. (Cited on pp. 14, 643.)
[353] S. S., Wilks. Mathematical Statistics (New York: John Wiley & Sons, Inc., 1962). (Cited on pp. 37, 59, 60, 305.)
[354] D., Williams. Probability with Martingales (Cambridge University Press, 1991). (Cited on pp. 268, 293, 308.)
[355] D., Williams. Weighing the Odds: A Course in Probability and Statistics (Cambridge University Press, 2001). (Cited on p. 14.)
[356] I. H., Witten and E., Frank. Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn (Morgan Kaufmann, 2005). (Cited on p. 616.)
[357] R.W., Wolff. Poisson arrivals see time averages. Operations Research, 30 (1982), 223–231. (Cited on pp. 413, 477.)
[358] R. W., Wolff. Stochastic Modeling and Theory of Queues (Englewood Cliffs, NJ: Prentice Hall, 1989). (Cited on pp. 418, 419, 730, 731.)
[359] E., Wong and B., Hajek. Stochastic Processes in Engineering Systems (Springer-Verlag, 1985). (Cited on p. 516.)
[360] R. A., Wooding. The multivariate distribution of complex normal variables. Biometrika, 43 (1956), 212–215. (Cited on pp. 177, 330.)
[361] J. M., Wozencraft and I. M., Jacobs. Principles of Communication Engineering (New York: John Wiley & Sons, Inc., 1965). (Cited on pp. 261, 268, 371.)
[362] C. F. J., Wu. On the convergence of the EM algorithm. Annals of Statistics, 11:1 (1983), 95–103. (Cited on p. 563.)
[363] R. D., Yates and D. J., Goodman. Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 2nd edn (John Wiley & Sons, Inc., 2004). (Cited on p. 37.)
[364] I., Youn, B. L., Mark, and D., Richards. A statistical approach to geolocation of Internet hosts. In Proceedings of 18th IEEE International Conference on Computer Communications and Networks (ICCCN'09), San Francisco, CA (August 2009). (Cited on pp. 150, 151.)
[365] S. Z., Yu. Hidden semi-Markov models. Artificial Intelligence, 174 (2010), 215–243. (Cited on p. 606.)
[366] S. Z., Yu and H., Kobayashi. A hidden semi-Markov model with missing data and multiple observation sequence for mobility tracking. Signal Processing, 83:2 (2003), 235–250. (Cited on pp. 451, 606.)
[367] S. Z., Yu and H., Kobayashi. Practical implementation of an efficient forward-backward algorithm for an explicit-duration hidden Markov model. IEEE Transactions on Signal Processing, 54:5 (2006), 1947–1951. (Cited on p. 606.)
[368] M., Zakai. Second-order properties of the pre-envelope and envelope processes. IRE Transactions on Information Theory, IT-6 (1960), 556–557. (Cited on p. 340.)
[369] L. M., Zeger and H., Kobayashi. A simplified EM algorithm for detection of CPM signals in a fading multipath channel. Wireless Networks, 8 (2002), 649–658. (Cited on p. 566.)
[370] X., Zhang. Space–time diversity in multiple-antenna wireless communication systems. PhD thesis, Department of Electrical Engineering, Princeton University, Princeton, NJ (June 2004). (Cited on p. 395.)
[371] L., Zweig. Speech recognition with dynamic bayesian networks. PhD thesis, University of California (1998). (Cited on p. 624.)