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Markov processes

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References

  1. Billingsley, P. (1968). Convergence of Probability Measures, Wiley, New York.

    Google Scholar 

  2. Breiman, L. (1968). Probability, Addison-Wesley, Reading, Massachusetts.

    Google Scholar 

  3. Breiman, L. (1986). Probability and Stochastic Processes, With a View Toward Applications, Second Edition. The Scientific Press, Palo Alto, California.

    Google Scholar 

  4. Cassandras, C.G. (1993). Discrete Event Systems: Modeling and Performance Analysis, Irwin, Boston.

    Google Scholar 

  5. Chellappa, R. and Jain, A., eds. (1993). Markov Random Fields: Theory and Application, Academic Press, San Diego.

    Google Scholar 

  6. Çinlar, E. (1975). Introduction to Stochastic Processes. Prentice-Hall, Englewood Cliffs, New Jersey.

    Google Scholar 

  7. Chung, K.L. (1967). Markov Chains with Stationary Transition Probabilities, Springer-Verlag, New York.

    Google Scholar 

  8. Feller, W. (1968). An Introduction to Probability Theory and Its Applications, Volume I, Third Edition. Wiley, New York.

    Google Scholar 

  9. Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Volume II, Second Edition. Wiley, New York.

    Google Scholar 

  10. Fox, B.L. (1990). “Generating Markov-Chain Transitions Quickly.” ORSA J. Comput, 2, 126–135.

    Google Scholar 

  11. Glynn, P.W. (1989). “A GSMP Formalism for Discrete Event Systems.” Proc. IEEE 77, 14–23.

    Google Scholar 

  12. Glynn, P.W. (1990). “Diffusion Approximations.” In Handbooks in OR and MS, Volume 2. D.P. Heyman and M.J. Sobel (eds.), Elsevier Science Publishers, Amsterdam, 145–198.

    Google Scholar 

  13. Grassmann, W.K. (1990). “Computational Methods in Probability,” In Handbooks in OR and MS, Volume 2, D.P. Heyman and M.J. Sobel (eds.). Elsevier Science Publishers, Amsterdam, 199–254.

    Google Scholar 

  14. Gross, D. and Miller, D.R. (1984). “The Randomization Technique as a Modelling Tool and Solution Procedure for Transient Markov Processes,” Oper. Res. 32, 343–361.

    Google Scholar 

  15. Heyman, D.P. and Sobel, M.J. (1982). Stochastic Models in Operations Research, Volume I: Stochastic Processes and Operating Characteristics. McGraw-Hill, New York.

    Google Scholar 

  16. Hordijk, A., Iglehart, D.L., and Schassberger, R. (1976). “Discrete-time methods for simulating continuous-time Markov chains,” Adv. Appl. Probab. 8, 772–778.

    Google Scholar 

  17. Howard, R. A. (1971). Dynamic Probabilistic Systems, Volume I: Markov Models, Wiley, New York.

    Google Scholar 

  18. Isaacson, D.L. and Madsen, R.W. (1976). Markov Chains: Theory and Applications, Wiley, New York.

    Google Scholar 

  19. Iosifescu, M. (1980). Finite Markov Processes and their Application, Wiley, New York.

    Google Scholar 

  20. Karlin, S. and Taylor, H.M. (1975). A First Course in Stochastic Processes, Second Edition. Academic Press, New York.

    Google Scholar 

  21. Karlin, S. and Taylor, H.M. (1981). A Second Course in Stochastic Processes, Academic Press, New York.

    Google Scholar 

  22. Keilson, J. (1979). Markov Chain Models — Rarity and Exponentiality, Springer-Verlag, New York.

    Google Scholar 

  23. Kelly, F.P. (1979). Reversibility and Stochastic Networks, Wiley, New York.

    Google Scholar 

  24. Kemeny, J.G. and Snell, J.L. (1976). Finite Markov Chains, Springer-Verlag, New York.

    Google Scholar 

  25. Kemeny, J.G., Snell, J.L., and Knapp, A.W. (1966). Denumerable Markov Chains, Van Nostrand, Princeton.

    Google Scholar 

  26. Kindermann, R. and Snell, J.L. (1980). Markov Random Fields and their Applications. American Mathematical Society, Providence, Rhode Island.

    Google Scholar 

  27. Maistrov, L.E. (1974). Probability Theory: A Historical Sketch, Academic Press, New York.

    Google Scholar 

  28. Neuts, M.F. (1981). Matrix-Geometric Solutions in Stochastic Models, The Johns Hopkins University Press, Baltimore.

    Google Scholar 

  29. Parzen, E. (1962). Stochastic Processes, Holden-Day, San Francisco.

    Google Scholar 

  30. Snell, J.L. (1988). Introduction to Probability, Random House, New York.

    Google Scholar 

  31. Whitt, W. (1980). “Continuity of Generalized Semi-Markov Processes.” Math. Opns. Res. 5, 494–501.

    Google Scholar 

  32. Whittle, P. (1986). Systems in Stochastic Equilibrium, Wiley, New York.

    Google Scholar 

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© 2001 Kluwer Academic Publishers

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Miller, D.R. (2001). Markov processes. In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, New York, NY. https://doi.org/10.1007/1-4020-0611-X_582

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  • DOI: https://doi.org/10.1007/1-4020-0611-X_582

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-7923-7827-3

  • Online ISBN: 978-1-4020-0611-1

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