Hidden Markov model: Difference between revisions

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A '''hidden Markov model''' ('''HMM''') is a [[statistical model]] where the system being modelled is assumed to be a [[Markov process]] with unknown parameters, and the challenge is to determine the hidden parameters, from the [[observable]] parameters, based on this assumption. 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 trnsitiontransition 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.

==State transitions in a hidden Markov model==