Redirected from Hidden Markov Model
The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications.
In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. A hidden Markov model adds outputs: each state has a probability distribution over the possible output tokens. Therefore, looking at a sequence of tokens generated by an HMM does not directly indicate the sequence of states.
There are 3 canonical problems to solve with HMMs:
Applications of hidden Markov models:
See also:
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