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[[File:HiddenMarkovModel.svg|right|thumb|300px|
Figure 1. Probabilistic parameters of a hidden Markov model (example)<br />
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In its discrete form, a hidden Markov process can be visualized as a generalization of the [[urn problem]] with replacement (where each item from the urn is returned to the original urn before the next step).<ref>{{cite journal |author=Lawrence R. Rabiner |author-link=Lawrence Rabiner |date=February 1989 |title=A tutorial on Hidden Markov Models and selected applications in speech recognition |url=http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf |journal=Proceedings of the IEEE |volume=77 |issue=2 |pages=257–286 |citeseerx=10.1.1.381.3454 |doi=10.1109/5.18626 |s2cid=13618539}} [http://www.cs.cornell.edu/courses/cs481/2004fa/rabiner.pdf]</ref> Consider this example: in a room that is not visible to an observer there is a genie. The room contains urns -X1,
The Markov process itself cannot be observed, only the sequence of labeled balls, thus this arrangement is called -a "hidden Markov process". This is illustrated by the lower part of the diagram shown in Figure 1, where one can see that balls
=== Weather guessing game ===
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