Hidden Markov model: Difference between revisions

Revision as of 14:08, 14 November 2004

Revision as of 14:11, 14 November 2004

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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.

== A concrete example ==

{{HMM example}}

''This examaple is further elaborated in [[Viterbi algorithm]] page''

==State transitions in a hidden Markov model==

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* Given the model parameters, find the most likely sequence of (hidden) states which could have generated a given output sequence. Solved by the [[Viterbi algorithm]].

* Given an output sequence, find the most likely set of state transition and output probabilities. Solved by the [[Baum-Welch algorithm]].

=== A concrete example ===

{{HMM example}}

''This examapleexample is further elaborated in [[Viterbi algorithm]] page''

===Applications of hidden Markov models===