Hidden Markov models: the theory
Hidden Markov models for sequence labeling

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.