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References

Published online by Cambridge University Press:  05 June 2012

Hisashi Kobayashi
Affiliation:
Princeton University, New Jersey
Brian L. Mark
Affiliation:
George Mason University, Virginia
William Turin
Affiliation:
AT&T Bell Laboratories, New Jersey
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Summary

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Type
Chapter
Information
Probability, Random Processes, and Statistical Analysis
Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance
, pp. 740 - 758
Publisher: Cambridge University Press
Print publication year: 2011

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References

[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[4] H., Akimaru and K., Kawashima. Teletraffic: Fundamentals and Applications (Tokyo: Institute of Electrical Communications, 1990). (In Japanese.) (Cited on p. 707.)Google Scholar
[5] R., Arens. Complex process for envelopes of normal noise. IRE Transactions on Information Theory, IT-3 (1957), 204–207. (Cited on p. 340.)Google Scholar
[6] S., Asmussen. Applied Probability and Queues (New York: Springer, 2003). (Cited on p. 268.)Google Scholar
[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.)Google Scholar
[8] K., Azuma. Weighted sums of certain dependent random variables. Tohoku Mathematics Journal, 19:3 (1967), 357–367. (Cited on p. 272.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[17] R. E., Bellman. Dynamic Programming (Princeton, NJ: Princeton University Press, 1957). (Cited on p. 592.)Google Scholar
[18] J. O., Berger. Statistical Decision Theory and Bayesian Analysis, 2nd edition (Springer-Verlag, 1995). (Cited on p. 104.)Google Scholar
[19] J. M., Bernardo and A. F. M., Smith. Bayesian Theory (Wiley, 1994). (Cited on p. 104.)Google Scholar
[20] P. L., Bernstein. Against the Gods: The Remarkable Story of Risk (John Wiley & Sons, 1996). (Cited on p. 12.)Google Scholar
[21] S. N., Bernstein. On certain modifications of Chebyshev's inequality. Doklady Akademii Nauk USSR, 17:6 (1937), 275–277. (Cited on p. 271.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[24] D., Bertsekas and J. N., Tsitsiklis, Introduction to Probability, 2nd edition (Athena Scientific, 2008). (Cited on p. 37.)Google Scholar
[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.)Google Scholar
[26] P., Billingsley. Probability and Measure, 3rd edn (John Wiley & Sons, 1995). (Cited on pp. 293, 305, 308.)Google Scholar
[27] P., Billingsley. Convergence of Probability Measures (New York: Wiley, 1999). (Cited on p. 202.)Google Scholar
[28] G., Birkhoff and S., MacLane. A Survey of Modern Algebra, 5th edn (New York: Macmillan, 1996). (Cited on p. 241.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[31] I. F., Blake. An Introduction to Applied Probability (New York: John Wiley & Sons, Inc., 1979). (Cited on p. 37.)Google Scholar
[32] R., Blossey. Statistical Mechanics for Biologists (Chapman & Hall/CRC, 2006). (Cited on p. viii.) xxviii.)Google Scholar
[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.)Google Scholar
[34] E., Borel. Les probabilités dénombrables et leurs applications arithmétiques. Rendiconti del Circolo Matematico di Palermo, 27 (1909), 247–271. (Cited on p. 300.)Google Scholar
[35] L., Breiman. Probability (Reading, MA: Addison-Wesley, 1968). (Cited on pp. 308, 516.)Google Scholar
[36] L., Breiman. Probability and Stochastic Processes with a View Toward Applications (Boston: Houghton Mifflin, 1969). (Cited on p. 690.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[40] S. L., Brumelle. Some inequalities for parallel server queues. Operations Research, 19:2 (1971), 402–413. (Cited on p. 730.)Google Scholar
[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.)Google Scholar
[42] J. A., Bucklew. Large Deviation Techniques in Decision, Simulation and Estimation (New York: John Wiley & Sons, Inc., 1990). (Cited on p. 268.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[45] F. A., Campbell and R. M., Foster. Fourier Integrals for Practical Applications (Princeton, NJ: Van Nostrand Company, Inc., 1948). (Cited on p. 194.)Google Scholar
[46] O., Cappé, E., Moulines, and T., Rydén. Inference in Hidden Markov Models (Springer, 2005). (Cited on p. 606.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[51] C., Chatfield. The Analysis of Time Series: Theory and Practice (London: Chapman & Hall, 1975). (Cited on p. 357.)Google Scholar
[52] K. L., Chung. Markov Chains: With Stationary Transition Probabilities (New York: Springer-Verlag, 1967). (Cited on p. 477.)Google Scholar
[53] K. L., Chung. A Course in Probability Theory (New York: Bruce & World, 1968). (Cited on p. 202.)Google Scholar
[54] K. L., Chung. A Course in Probability Theory, 2nd edn (New York: Academic Press, 1974). (Cited on p. 308.)Google Scholar
[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.)Google Scholar
[56] E., Çinlar. Markov renewal theory. Advances in Applied Probability, 1 (1969), 123–187. (Cited on pp. 457, 477.)Google Scholar
[57] E., Çinlar. Introduction to Stochastic Processes (Englewood Cliffs, NJ: Prentice Hall, 1975). (Cited on pp. 418, 451, 477.)Google Scholar
[58] E., Çinlar. Markov renewal theory: a survey. Management Science, 21:7 (1975), 727–752. (Cited on pp. 457, 477, 478.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[61] J. W., Cohen. The Single Server Queue, 2nd edn (Amsterdam: North-Holland, 1982). (Cited on p. 730.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[67] R. B., Cooper. Introduction to Queueing Theory, 2nd edn (New York: North-Holland, 1981). (Cited on p. 731.)Google Scholar
[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.)Google Scholar
[69] T. M., Cover and J. A., Thomas. Elements of Information Theory (New York: John Wiley & Sons, Inc., 1991). (Cited on p. 247.)Google Scholar
[70] D. R., Cox. Renewal Theory (Methuen, 1962). (Cited on p. 418.)Google Scholar
[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.)Google Scholar
[72] D. R., Cox and H. D., Miller. The Theory of Stochastic Processes (New York: John Wiley & Sons, Inc., 1965). (Cited on p. 516.)Google Scholar
[73] D. R., Cox and W. L., Smith. Queues (London: Methuen, 1961). (Cited on pp. 707, 731.)Google Scholar
[74] H., Cramér. Mathematical Methods of Statistics (Princeton, NJ: Princeton University Press, 1946). (Cited on pp. 305, 308, 532, 533, 536, 549.)Google Scholar
[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.)Google Scholar
[76] J. N., Daigle. Queueing Theory for Telecommunications (Reading, MA: Addison-Wesley, 1992). (Cited on p. 731.)Google Scholar
[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.)Google Scholar
[78] F. N., David. Games, Gods and Gambling (London: Charles Griffin & Co., 1962). (Cited on pp. 7, 14.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[81] P. A. M., Dirac. Principles of Quantum Mechanics (Oxford, UK: Oxford University Press, 1935). (Cited on p. 46.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[84] J., Dugundji. Envelope and pre-envelope of real waveforms. IRE Transactions on Information Theory, IT-4 (1958), 53–57. (Cited on p. 340.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[87] A., Einstein. On the theory of Brownian motion. Annalen der Physik, 19 (1906), 371–381. (Cited on p. 516.)Google Scholar
[88] A., Einstein. Investigations of the Theory of the Brownian Movement (translated by A. D., Cowper) (New York: Dover, 1956). (Cited on p. 499.)Google Scholar
[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.)Google Scholar
[90] R., Ellis. Entropy, Large Deviations, and Statistical Mechanics (New York: Springer-Verlag, 2006). (Cited on p. 268.)Google Scholar
[91] T., Engset. Die Wahrscheinlichkeitsrechung zur Bestimmung der Wähleranzahl in automatischen Fernsprechämtern. Electrotechnische Zeitschrift, 31 (1918), 304–305. (Cited on p. 722.)Google Scholar
[92] Y., Ephraim. Statistical-model-based speech enhancement systems. Proceedings of the IEEE, 80:10 (1992), 1526–1555. (Cited on p. 605.)Google Scholar
[93] Y., Ephraim and N., Merhav. Hidden Markov processes. IEEE Transactions on Information Theory, 48:6 (2002), 1518–1569. (Cited on pp. 592, 606.)Google Scholar
[94] A. K., Erlang. The theory of probabilities and telephone conversations. Nyt Tidsskrift for Matematik B, 20 (1909), 33–39. (Cited on p. 695.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[97] W., Feller. Über den zentralen Grenzwertsatz der Wahrscheinlichkeitsrechnung. Mathematische Zeitschrift, 40 (1935), 521–559. (Cited on p. 305.)Google Scholar
[98] W., Feller. A direct proof of Stirling's formula. The American Mathematical Monthly, 74:10 (1967), 1223–1225. (Cited on p. 275.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[101] J., Felsenstein. Evolutionary trees from DNA sequences. Journal of Molecular Evolution, 17 (1981), 368–376. (Cited on p. 476.)Google Scholar
[102] J., Felsenstein. Inferring Phylogenies (Sunderland, MA: Sinauer Associates, 2004). (Cited on p. 477.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[106] R. A., Fisher. Design of Experiments, vol. 1, 3rd edn (Edinburgh: Oliver and Boyd, 1935). (Cited on p. 164.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[111] J., Franklin. The Science of Conjecture: Evidence and Probability before Pascal (Baltimore: The John Hopkins Press, 2001). (Cited on p. 14.)Google Scholar
[112] H., Freeman. Discrete-Time Systems (New York: John Wiley & Sons, Inc., 1965). (Cited on p. 211.)Google Scholar
[113] R. G., Gallager. Information Theory and Reliable Communications (New York: John Wiley & Sons, Inc., 1968). (Cited on p. 268.)Google Scholar
[114] D. P., Gaver. Diffusion approximation methods for certain congestion problems. Journal of Applied Probability, 5 (1968), 607–623. (Cited on p. 516.)Google Scholar
[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.)Google Scholar
[116] E., Gelenbe and I., Mitrani. Analysis and Synthesis of Computer Systems (Academic Press, 1980). (Cited on p. 731.)Google Scholar
[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.)Google Scholar
[119] I. I., Gikhman and A. V., Skorokhod. Introduction to The Theory of Random Processes (W. B., Saunders Company, 1969). (Cited on p. 690.)Google Scholar
[120] E. N., Gilbert. Capacity of a burst-noise channel. Bell System Technical Journal, 39 (1960), 1253–1265. (Cited on p. 582.)Google Scholar
[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.)Google Scholar
[122] B. V., Gnedenko. Theory of Probability (New York: Chelsea, 1962). (Cited on p. 308.)Google Scholar
[123] B. V., Gnedenko and N., Kolmogorov. Limit Distributions for Sums of Independent Random Variables (Reading, MA: Addison Wesley, 1954). (Cited on p. 202.)Google Scholar
[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.)Google Scholar
[125] R. M., Gray. Toeplitz and Circulant Matrices: A Review (Norwell, MA: Now Publishers, 2006). (Cited on pp. 361, 362.)Google Scholar
[126] R. M., Gray and L., Davisson. An Introduction to Statistical Signal Processing (Cambridge University Press, 2004). (Cited on p. 37.)Google Scholar
[127] D., Green and J., Swets. Signal Detection Theory and Psychophysics (New York: John Wiley and Sons Inc., 1966). (Cited on p. 542.)Google Scholar
[128] E., Greenberg. Introduction to Bayesian Econometrics (Cambridge University Press, 2008). (Cited on pp. xxviii, 14, 643.)Google Scholar
[129] U., Grenander and G., Szego. Toeplitz Forms and Their Applications, 2nd edn (New York: Chelsea, 1984). (Cited on p. 361.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[135] I., Hacking. The Emergence of Probability (New York: Cambridge University Press, 1975). (Cited on p. 14.)Google Scholar
[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.)Google Scholar
[139] A., Hald. Statistical Theory with Engineering Applications (New York: John Wiley & Sons, Inc., 1952). (Cited on pp. 142, 153, 549.)Google Scholar
[140] A., Hald. A History of Probability and Statistics, and Their Applications before 1750 (New York: Wiley, 1990 and 2003). (Cited on p. 14.)Google Scholar
[141] J. D., Hamilton. Time Series Analysis (Princeton, NJ: Princeton University Press, 1994). (Cited on pp. 322, 389, 395, 566.)Google Scholar
[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.)Google Scholar
[143] J. M., Hammersley and D. C., Handscomb. Monte Carlo Methods (London: Methuen, 1964). (Cited on p. 523.)Google Scholar
[144] E. J., Hannan. Time-Series Analysis (London: Methuen, 1960). (Cited on p. 357.)Google Scholar
[145] A. C., Harvey. Forecasting, Structural Time Series Models and the Kalman Filter (Cambridge University Press, 1989). (Cited on pp. 322, 389, 395.)Google Scholar
[146] B. R., Haverkort. Performance of Computer Communication Systems: A Model-Based Approach (New York: John Wiley & Sons, Inc., 1998). (Cited on p. 731.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[149] S., Haykin. Neural Networks: A Comprehensive Foundation (Upper Saddle River, NJ: Prentice Hall, 1999). (Cited on pp. 3, 616.)Google Scholar
[150] C. W., Helstrom. Statistical Theory of Signal Detection (Pergamon Press, 1960). (Cited on pp. 177, 340, 542, 549.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[153] K., Hoffman and R., Kunze. Linear Algebra (Englewood Cliffs, NJ: Prentice Hall, 1961). (Cited on p. 241.)Google Scholar
[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.)Google Scholar
[155] J. Y., Hui. Switching and Traffic Theory for Integrated Broadband Networks (Boston, MA: Kluwer, 1990). (Cited on pp. 268, 737.)Google Scholar
[156] Ibm, . IBM Subroutine Library – Mathematics, User's Guide, SH12-5300-1, 2nd edn (IBM Corporation, 1974). (Cited on p. 234.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[161] D. L., Jagerman. An inversion technique for the Laplace transform. Bell System Technical Journal, 61 (1982), 1995–2002. (Cited on p. 234.)Google Scholar
[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.)Google Scholar
[163] F., Jelinek. Continuous speech recogniton by statistical methods. Proceedings of the IEEE, 64 (1976), 532–556. (Cited on p. 605.)Google Scholar
[164] F., Jelinek. Statistical Methods for Speech Recognition (MIT Press, 1998). (Cited on pp. 425, 605, 615.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[168] W. S., Jewell. A simple proof of L = λW. Operations Research, 15:6 (1967), 1109–1116. (Cited on p. 730.)Google Scholar
[169] I. T., Jolliffe. Principal Component Analysis, 2nd edn (New York: Springer, 2002). (Cited on p. 395.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[173] E. P. C., Kao. An Introduction to Stochastic Processes (Belmont, CA: Duxbury Press, 1979). (Cited on pp. 418, 512.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[176] J., Kay. The EM algorithm in medical imaging. Statistical Methods in Medical Research, 6:1 (1975), 55–75. (Cited on p. 566.)Google Scholar
[177] F. P., Kelly. Reversibility and Stochastic Networks (John Wiley & Sons, Inc., 1979). (Cited on pp. 477, 704, 719, 731.)Google Scholar
[178] F. P., Kelly. Loss networks (invited paper). The Annals of Applied Probability, 1 (1991), 319–378. (Cited on p. 724.)Google Scholar
[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.)Google Scholar
[180] M., Kijima. Markov Processes for Stochastic Modeling (Chapman & Hall, 1997). (Cited on p. 477.)Google Scholar
[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.)Google Scholar
[183] P. J. B., King. Computer and Communication System Performance Modeling (Englewood Cliffs, NJ: Prentice Hall, 1990). (Cited on p. 731.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[186] J. F. C., Kingman. Poisson Processes (Clarendon Press, 1992). (Cited on p. 10.)Google Scholar
[187] J., Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46:5 (1999), 604–632. (Cited on pp. 384, 395.)Google Scholar
[188] L., Kleinrock. Time-shared systems: a theoretical treatment. Journal of the ACM, 14 (1967), 242–261. (Cited on p. 716.)Google Scholar
[189] L., Kleinrock. Queueing Systems, Vol. I: Theory (New York: JohnWiley & Sons, Inc., 1975). (Cited on pp. 235, 419, 451, 731.)Google Scholar
[190] L., Kleinrock. Queueing Systems, Vol. II: Computer Applications (New York: John Wiley & Sons, Inc., 1976). (Cited on pp. 235, 724.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[205] P., Koehn. Statistical Machine Translation (Cambridge University Press, 2010). (Cited on p. 566.)Google Scholar
[206] D., Koller and N., Friedman. Probabilistic Graphical Models (Cambridge, MA: MIT Press, 2009). (Cited on pp. 3, 14, 643.)Google Scholar
[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.)Google Scholar
[208] A. N., Kolmogorov. Grundbegriffe der Wahrscheinlichkeitsrechnung (Berlin: Julius Springer, 1933). (Cited on pp. 9, 20.)Google Scholar
[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.)Google Scholar
[210] A. N., Kolmogorov. Foundations of the Theory of Probability (translated by Nathaniel Morrison) (New York: Chelsea, 1950). (Cited on p. 9.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[215] S., Kullback. Information Theory and Statistics (New York, NY: John Wiley & Sons, Inc., 1959). (Cited on p. 557.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[219] S. S., Lavenberg, ed. Computer Performance Modeling Handbook (Orlando, FL: Academic Press, 1983). (Cited on p. 731.)Google Scholar
[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.)Google Scholar
[221] E. L., Lehmann. Testing Statistical Hypotheses (New York: Springer, 1986). (Cited on p. 549.)Google Scholar
[222] A., Leon-Garcia. Probability and Random Processes for Electrical Engineering, 2nd edn (Reading, MA: Addison-Wesley, 1994). (Cited on pp. 37, 549.)Google Scholar
[223] D. A., Levin, Y., Peres, and E. L., Wilmer. Markov Chains and Mixing Times (American Mathematical Society, 2008). (Cited on p. 635.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[226] J. W., Lindeberg. Eine neue Herleitung des Exponentialgesetzes in der Wahrscheinlichkeitsrechnung. Mathematische Zeitschrift, 15 (1922), 211–225. (Cited on p. 305.)Google Scholar
[227] L., Lipsky. Queueing Theory: A Linear Algebraic Approach (New York: MacMillan, 1992). (Cited on p. 731.)Google Scholar
[228] J. D. C., Little. A proof of the queueing formula L = λW. Operations Research, 9 (1961), 383–387. (Cited on p. 696.)Google Scholar
[229] M., Loève. Sur les fonctions aléatoires du second ordre. Revue Scientifique, 83 (1945), 297–303. (Cited on p. 365.)Google Scholar
[230] M., Loève. Probability Theory (Princeton, NJ: D. Van Nostrand, 1955). (Cited on p. 308.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[234] D. J. C., MacKay. Information Theory, Inference, and Learning Algorithms (Cambridge University Press, 2003). (Cited on pp. 14, 643.)Google Scholar
[235] L. E., Maistrov. Probability Theory: A Historical Sketch (New York: Academic Press, 1974). (Cited on p. 14.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.) 752Google Scholar
[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.)Google Scholar
[241] W. N., Martin and W. M., Spears, eds. Foundations of Genetic Algorithms (Morgan and Kaufmann/Academic Press, 2001). (Cited on p. 616.)Google Scholar
[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.)Google Scholar
[243] J. H., Matthews and R. W., Howell. Complex Analysis for Mathematics and Engineering (Jones & Bartlett Publishers, Inc., 2006). (Cited on p. 195.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[246] G., McLachlan and T., Krishnan. The EM Algorithm and Exensions (John Wiley & Sons, 1997). (Cited on pp. 563, 564, 566.)Google Scholar
[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.)Google Scholar
[248] D., Middleton. An Introduction to Statistical Communication Theory (New York: McGraw- Hill, 1960). (Cited on p. 549.)Google Scholar
[249] N. M., Mirasol. The output of an M/G/∞ queue is Poisson. Operations Research, 11 (1963), 282–284. (Cited on p. 733.)Google Scholar
[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.)Google Scholar
[251] P. M., Morse. Queues, Inventries and Maintenance (New York:Wiley & Sons, 1958). (Cited on p. 731.)Google Scholar
[252] M. E., Munroe. Introduction to Measure and Integration (Reading, MA: Addison-Wesley, 1953). (Cited on p. 293.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[255] G. F., Newell. Applications of Queueing Theory (London: Chapman & Hall, 1971). (Cited on p. 516.)Google Scholar
[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.)Google Scholar
[257] H., Nyquist. Certain topics in telegraph transmission theory. Transactions of the AIEE, 47 (1928), 363–390. (Cited on p. 353.)Google Scholar
[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.)Google Scholar
[259] J. K., Omura. On the Viterbi decoding algorithm. IEEE Transactions on Information Theory, IT-15 (1969), 77–179. (Cited on p. 592.)Google Scholar
[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.)Google Scholar
[261] M. F. M., Osborne. Brownian motion in the stock market. Operations Research, 7:2 (1959), 145–173. (Cited on p. 5.)Google Scholar
[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.)Google Scholar
[263] E., Parzen. Stochastic Processes (San Francisco: Holden-Day, Inc., 1962). (Cited on p. 323.)Google Scholar
[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.)Google Scholar
[265] J., Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (San Mateo, CA: Morgan Kaufmann Publishers, 1988). (Cited on p. 643.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[268] M., Pepe. The Statistical Evaluation of Medical Tests for Classification and Prediction (New York: Oxford University Press, 2003). (Cited on p. 542.)Google Scholar
[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.)Google Scholar
[270] J., Piasecki. Centenary of Marian Smoluchowski. Acta Physica Polonica B, 38:5 (2007), 1623–1629. (Cited on p. 11.)Google Scholar
[271] H. V., Poor. An Introduction to Signal Detection and Estimation (Springer, 1994). (Cited on p. 549.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[275] L. R., Rabiner and B. H., Juang. Fundamentals of Speech Recogntion (Prentice Hall, 1993). (Cited on pp. 605, 615.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[284] V., Romanovsky. Diskretnye tsepi Markova (Moscow: Gostekhizdat, 1949). (Cited on p. 605.)Google Scholar
[285] V., Romanovsky. Discrete Markov Chain (translated by E., Senata) (Groningen: Wolters-Noordhoff, 1970). (Cited on p. 605.)Google Scholar
[286] M., Rosenblatt. Random Processes (New York: Oxford University Press, 1962). (Cited on p. 451.)Google Scholar
[287] S. M., Ross. Stochastic Processes (New York: John Wiley & Sons, Inc., 1983). (Cited on p. 719.)Google Scholar
[288] S. M., Ross. Stochastic Processes, 2nd edn (New York: John Wiley & Sons, Inc., 1996). (Cited on pp. 272, 273, 418, 451, 516.)Google Scholar
[289] S. M., Ross. A First Course in Probability, 6th edn (Prentice Hall, 2002). (Cited on pp. 37, 66, 104, 268, 271.)Google Scholar
[290] H. L., Royden. Real Analysis (Prentice Hall, 1988). (Cited on p. 293.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[293] R., Schapire. Strength of weak learnability. Machine Learning, 5:2 (1990), 197–227. (Cited on p. 616.)Google Scholar
[294] A., Schuster. On lunar and solar periodities of earthquakes. Proceedings of the Royal Society, 61 (1897), 455–465. (Cited on p. 357.)Google Scholar
[295] M., Schwartz, W. R., Bennet, and S., Stein. Communication Systems and Techniques (New York: Wiley, 1995). (Cited on pp. 336, 340.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[298] G., Shafer. The Art of Causal Conjecture (MIT Press, 1996). (Cited on p. 477.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[302] W. T., Shaw. Complex Analysis with Mathematica (Cambridge University Press, 2006). (Cited on p. 195.)Google Scholar
[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.)Google Scholar
[304] A., Shwartz and A., Weiss. Large Deviations for Performance Analysis (Chapman & Hall, 1995). (Cited on p. 268.)Google Scholar
[305] C. A., Sims. Macroeconomics and reality. Econometrica, 48:1 (1980), 1–48. (Cited on p. 5.)Google Scholar
[306] D., Skillicorn. Understanding Complex Datasets: Data Mining with Matrix Decomposition (Chapman & Hall/CRC, 2007). (Cited on p. 395.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[312] S. M., Stigler. The History of Statistics: The Measurement of Uncertainty before 1900 (Cambridge, MA: Harvard University Press, 1986). (Cited on p. 14.)Google Scholar
[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.)Google Scholar
[314] (W. S., Gosset). The probable error of a mean. Biometrika, 6:1 (1908), 1–25. (Cited on p. 162.)Google Scholar
[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.)Google Scholar
[316] R., Syski. Introduction to Congestion Theory in Telephone Systems, 2nd edn (Amsterdam: North-Holland, 1986). (Cited on pp. 720, 731.)Google Scholar
[317] L., Takács. Introduction to the Theory of Queues (New York: Oxford University Press, 1962). (Cited on p. 731.)Google Scholar
[318] S., Tezuka. Uniform Random Numbers: Theory and Practice (Norwell, MA: Kluwer Academic Publishers, 1995). (Cited on p. 131.)Google Scholar
[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.)Google Scholar
[320] L., Tierney. Markov chains for exploring posterior distributions. Annals of Statistics, 22:4 (1994), 1701–1728. (Cited on p. 637.)Google Scholar
[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.)Google Scholar
[322] H. C., Tijms. Stochastic Modeling and Analysis (John Wiley & Sons, Inc., 1986). (Cited on p. 731.)Google Scholar
[323] E. C., Titchmarsh. Theory of Functions (London: Oxford University Press, 1939). (Cited on p. 194.)Google Scholar
[324] E. C., Titchmarsh. Introduction to the Theory of Fourier Integrals (London: Oxford University Press, 1948). (Cited on p. 345.)Google Scholar
[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.)Google Scholar
[326] ,Tree of Life Web Project. WWW page, August 2010. http://tolweb.org. (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.)Google Scholar
[328] J. W., Tukey. Exploratory Data Analysis (Reading, MA: Addison-Wesley, 1977). (Cited on p. 153.)Google Scholar
[329] G. L., Turin. On optimal diversity reception. IRE Transactions on Information Theory, IT-7 (1961), 154–167. (Cited on pp. 177, 322.)Google Scholar
[330] W., Turin. Digital Transmission Systems: Performance Analysis and Modeling (McGraw Hill, 1999). (Cited on p. 432.)Google Scholar
[331] W., Turin. MAP decoding in channels with memory. IEEE Transactions on Communications, 48:5 (2000), 757–763. (Cited on p. 566.)Google Scholar
[332] W., Turin. Performance Analysis and Modeling of Digital Transmission Systems (Kluwer Academic/Plenum Pulishers, 2004). (Cited on pp. 456, 566, 606.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[335] P., Valkó. Numerical inversion of Laplace transform: a challenge for developers of numerical methods. http://pumpjack.tamu.edu/∼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.)Google Scholar
[337] V., Vapnik. The Nature of Statistical Learning Theory (New York: Springer-Verlag, 1995). (Cited on p. 643.)Google Scholar
[338] V., Vapnik. Statistical Learning Theory (New York: John Wiley, 1998). (Cited on pp. 3, 616, 643.)Google Scholar
[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.)Google Scholar
[340] A. J., Viterbi. A personal history of the Viterbi algorithm. IEEE Signal Processing Magazine, 23:4 (2006), 120–142. (Cited on p. 605.)Google Scholar
[341] R., von Mises. Wahrscheinlichkeitsrechnung, Statistik und Wahrheit (Wien: Verlang von Julius Springer, 1928). (Cited on p. 19.)Google Scholar
[342] R., von Mises. Probability, Statistics and Truth (New York: MacMillan, 1954) (Translation of the 1928 publication.) (Cited on p. 9.)Google Scholar
[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.)Google Scholar
[345] J., Walrand. An Introduction to Queueing Networks (Englewood Cliffs, NJ: Prentice Hall, 1988). (Cited on p. 479.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[349] N., Wiener. The Fourier Integral and Certain of Its Applications (Cambridge University Press, 1933). (Reissued in 1988.) (Cited on p. 194.)Google Scholar
[350] N., Wiener. Extrapolation, Interpolation, and Smoothing of Stationary Time Series (New York: John Wiley, 1949). (Cited on pp. 13, 645, 656, 690.)Google Scholar
[351] N., Wiener. Collected Works, Vol. I (Edited by P. R. Masani) (Cambridge, MA: MIT Press, 1976). (Cited on p. 11.)Google Scholar
[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.)Google Scholar
[353] S. S., Wilks. Mathematical Statistics (New York: John Wiley & Sons, Inc., 1962). (Cited on pp. 37, 59, 60, 305.)Google Scholar
[354] D., Williams. Probability with Martingales (Cambridge University Press, 1991). (Cited on pp. 268, 293, 308.)Google Scholar
[355] D., Williams. Weighing the Odds: A Course in Probability and Statistics (Cambridge University Press, 2001). (Cited on p. 14.)Google Scholar
[356] I. H., Witten and E., Frank. Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn (Morgan Kaufmann, 2005). (Cited on p. 616.)Google Scholar
[357] R.W., Wolff. Poisson arrivals see time averages. Operations Research, 30 (1982), 223–231. (Cited on pp. 413, 477.)Google Scholar
[358] R. W., Wolff. Stochastic Modeling and Theory of Queues (Englewood Cliffs, NJ: Prentice Hall, 1989). (Cited on pp. 418, 419, 730, 731.)Google Scholar
[359] E., Wong and B., Hajek. Stochastic Processes in Engineering Systems (Springer-Verlag, 1985). (Cited on p. 516.)Google Scholar
[360] R. A., Wooding. The multivariate distribution of complex normal variables. Biometrika, 43 (1956), 212–215. (Cited on pp. 177, 330.)Google Scholar
[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.)Google Scholar
[362] C. F. J., Wu. On the convergence of the EM algorithm. Annals of Statistics, 11:1 (1983), 95–103. (Cited on p. 563.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[365] S. Z., Yu. Hidden semi-Markov models. Artificial Intelligence, 174 (2010), 215–243. (Cited on p. 606.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)Google Scholar
[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.)