Abstract
We have seen in Chapter 16 that an important random process is the IID random process. When applicable to a specific problem, it lends itself to a very simple analysis. A Bernoulli random process,which consists of independent Bernoulli trials,is the archetypical example of this. In practice, it is found,however,that there is usually some dependence between samples of a random process. In Chapters 17 and 18 we modeled this dependence using wide sense stationary random process theory,but restricted the modeling to only the first two moments.
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Ā© 2012 Steven M. Kay
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Kay, S.M. (2012). Markov Chains. In: Intuitive Probability and Random Processes Using MATLABĀ®. Springer, Boston, MA. https://doi.org/10.1007/0-387-24158-2_22
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DOI: https://doi.org/10.1007/0-387-24158-2_22
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-24157-9
Online ISBN: 978-0-387-24158-6
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