Online Expectation Maximization based algorithms for inference in hidden Markov models

Sylvain Le Corff (1) , Gersende Fort (1)

1 LTCI - Laboratoire Traitement et Communication de l'Information

The Expectation Maximization (EM) algorithm is a versatile tool for model parameter estimation in latent data models. When processing large data sets or data stream however, EM becomes intractable since it requires the whole data set to be available at each iteration of the algorithm. In this contribution, a new generic online EM algorithm for model parameter inference in general Hidden Markov Model is proposed. This new algorithm updates the parameter estimate after a block of observations is processed (online). The convergence of this new algorithm is established, and the rate of convergence is studied showing the impact of the block size. An averaging procedure is also proposed to improve the rate of convergence. Finally, practical illustrations are presented to highlight the performance of these algorithms in comparison to other online maximum likelihood procedures.

Mots clés: Hidden Markov models; Online estimation; Expectation Maximization

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Sylvain Le Corff, Gersende Fort. Online Expectation Maximization based algorithms for inference in hidden Markov models. Electronic Journal of Statistics , 2013. ⟨hal-00615270v3⟩