Abstract
Markov† chains are the most substantial application of conditional probability which is easily accessible, and, at the same time, they provide an excellent introduction to the more general subject of stochastic processes. A stochastic process is a random variable with a time index (say, Xn, n= 0, 1, 2,...) for discrete time, or a family of random variables (say, X(t), 0<t<∞) for continuous time.
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© 1981 Plenum Press, New York
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Haight, F.A. (1981). Markov Chains. In: Applied Probability. Mathematical Concepts and Methods in Science and Engineering, vol 23. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6467-6_3
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DOI: https://doi.org/10.1007/978-1-4615-6467-6_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4615-6469-0
Online ISBN: 978-1-4615-6467-6
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