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==Hidden Markov model==
A [[hidden Markov model]] is a Markov chain for which the state is only partially observable. In other words, observations are related to the state of the system, but they are typically insufficient to precisely determine the state. Several well-known algorithms for hidden Markov models exist. For example, given a sequence of observations, the [[Viterbi algorithm]] will compute the most-likely corresponding sequence of states, the [[forward algorithm]] will compute the probability of the sequence of observations, and the [[Baum–Welch algorithm]] will estimate the starting probabilities, the transition function, and the observation function of a hidden Markov model. One common use of hidden Markov models is for voice recognition. An example of a medical application of the hidden Markov model is BioQT, an EKG analysis system originating from Oxford University's Department of Electrical Engineering and assigned to OBS Medical in the US for marketing. Covance, the clinical research organization, licensed the technology from OBS in 2006 and marketed it in 2007 within its Cardiac Safety Services division before selling the division to a competitor.
==Markov decision process==
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