Abstract
In this chapter we consider an important type of stochastic process called the Markov process. A Markov process1 is a stochastic process that has a limited form of “historical” dependency. To precisely define this dependency, let {X(t) : t ∈ T} be a stochastic process defined on the parameter set T. We will think of T in terms of time, and the values that X(t) can assume are called states which are elements of a state space S.
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Bibliographic Notes
J.G. Kemeny and J.L. Snell. Finite Markov Chains. Springer-Verlag, 1976.
J.G. Kemeny, J. L. Snell, and A. W. Knapp. Denumerable Markov Chains. Springer-Verlag, 1976.
L. Takacs. Combinatorial Methods in the Theory of Stochastic Processes. Robert E. Krieger, 1977.
E. Wong and B. Hajek. Stochastic Processes in Engineering Systems. Springer-Verlag, 1985.
S. Karlin and H. M. Taylor. A Second Course in Stochastic Processes. Academic Press, 1981.
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© 1995 Springer Science+Business Media New York
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Nelson, R. (1995). Markov Processes. In: Probability, Stochastic Processes, and Queueing Theory. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2426-4_8
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DOI: https://doi.org/10.1007/978-1-4757-2426-4_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-2846-7
Online ISBN: 978-1-4757-2426-4
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