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''a'' — state transition probabilities<br/>
''b'' — output probabilities]]
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}} [
The Markov process 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 y1, y2, y3, y4 can be drawn at each state. Even if the observer knows the composition of the urns and has just observed a sequence of three balls, ''e.g.'' y1, y2 and y3 on the conveyor belt, the observer still cannot be ''sure'' which urn (''i.e.'', at which state) the genie has drawn the third ball from. However, the observer can work out other information, such as the likelihood that the third ball came from each of the urns.
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