Abstract
Many decisions must be made within the context of randomness. Random failures of equipment, fluctuating production rates, and unknown demands are all part of normal decision making processes. In an effort to quantify, understand, and predict the effects of randomness, the mathematical theory of probability and stochastic processes has been developed, and in this chapter, one special type of stochastic process called a Markov chain is introduced.
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References
Galton, F., and Watson, H.W. (1875). On the Probability of the Extinction of Families, J.Anthropol. Soc. London, 4:138–144.
Miller, G.A. (1952). Finite Markov Processes in Psychology, Psychometrika 17:149–167.
Parzen, E. (1962). Stochastic Processes, Holden-Day, Inc., Oakland, CA.
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© 2010 Springer-Verlag Berlin Heidelberg
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Feldman, R.M., Valdez-Flores, C. (2010). Markov Chains. In: Applied Probability and Stochastic Processes. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05158-6_5
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DOI: https://doi.org/10.1007/978-3-642-05158-6_5
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Online ISBN: 978-3-642-05158-6
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