3. Hidden Markov models: the theory
We begin by presenting the principle of hidden Markov models, detailing the assumptions on which they are based and illustrating how they work with a few examples. Then, in keeping with tradition, we present hidden Markov models in terms of the "three problems" that need to be solved in order to use them in practice:
calculating the probability of a sequence of observations;
inference of the optimal state sequence given a sequence of observations ;
learning the parameters of a hidden Markov model from a corpus of training sequences.
Finally, we mention a few particularly popular variants of hidden Markov models.
3.1 Definition and principle
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Hidden Markov models: the theory
Hidden Markov models in practice
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