An EM and a stochastic version of the EM algorithm for nonparametric Hidden semi-Markov models

Abstract : The Hidden semi-Markov models (HSMMs) have been introduced to overcome the constraint of a geometric sojourn time distribution for the different hidden states in the classical hidden Markov models. Several variations of HSMMs have been proposed that model the sojourn times by a parametric or a nonparametric family of distributions. In this article, we concentrate our interest on the nonparametric case where the duration distributions are attached to transitions and not to states as in most of the published papers in HSMMs. Therefore, it is worth noticing that here we treat the underlying hidden semi–Markov chain in its general probabilistic structure. In that case, Barbu and Limnios (2008) proposed an Expectation–Maximization (EM) algorithm in order to estimate the semi-Markov kernel and the emission probabilities that characterize the dynamics of the model. In this paper, we consider an improved version of Barbu and Limnios' EM algorithm which is faster than the original one. Moreover, we propose a stochastic version of the EM algorithm that achieves comparable estimates with the EM algorithm in less execution time. Some numerical examples are provided which illustrate the efficient performance of the proposed algorithms.
Type de document :
Article dans une revue
Communications in Statistics - Simulation and Computation, Taylor & Francis, 2010, 39 (2), pp.240-261. 〈10.1080/03610910903411185〉
Liste complète des métadonnées

Littérature citée [45 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00576514
Contributeur : Samis Trevezas <>
Soumis le : lundi 14 mars 2011 - 15:04:17
Dernière modification le : mercredi 29 novembre 2017 - 09:21:57
Document(s) archivé(s) le : mercredi 15 juin 2011 - 03:03:12

Fichier

EM_SEM_HSMM.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Sonia Malefaki, Samis Trevezas, Nikolaos Limnios. An EM and a stochastic version of the EM algorithm for nonparametric Hidden semi-Markov models. Communications in Statistics - Simulation and Computation, Taylor & Francis, 2010, 39 (2), pp.240-261. 〈10.1080/03610910903411185〉. 〈inria-00576514〉

Partager

Métriques

Consultations de la notice

106

Téléchargements de fichiers

99