Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Quantifying and localizing state uncertainty in hidden Markov models using conditional entropy profiles

Jean-Baptiste Durand 1, 2 Yann Guédon 2, 3
1 MISTIS [2007-2015] - Modelling and Inference of Complex and Structured Stochastic Systems [2007-2015]
Inria Grenoble - Rhône-Alpes, LJK [2007-2015] - Laboratoire Jean Kuntzmann [2007-2015], Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019]
2 VIRTUAL PLANTS - Modeling plant morphogenesis at different scales, from genes to phenotype
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
Abstract : Abstract. A family of graphical hidden Markov models that generalizes hidden Markov chain (HMC) and tree (HMT) models is introduced. It is shown that global uncertainty on the state process can be decomposed as a sum of conditional entropies that are interpreted as local contributions to global uncertainty. An efficient algorithm is derived to compute conditional entropy profiles in the case of HMC and HMT models. The relevance of these profiles and their complementarity with other state restoration algorithms for interpretation and diagnosis of hidden states is highlighted. It is also shown that classical smoothing profiles (posterior marginal probabilities of the states at each time, given the observations) cannot be related to global state uncertainty in the general case.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

Littérature citée [8 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01058278
Déposant : Jean-Baptiste Durand <>
Soumis le : mardi 26 août 2014 - 14:39:26
Dernière modification le : lundi 20 juillet 2020 - 09:16:02
Document(s) archivé(s) le : jeudi 27 novembre 2014 - 16:11:42

Fichier

compstat2014_guedon.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01058278, version 1
  • PRODINRA : 314098

Citation

Jean-Baptiste Durand, Yann Guédon. Quantifying and localizing state uncertainty in hidden Markov models using conditional entropy profiles. COMPSTAT 2014 - 21st International Conference on Computational Statistics, The International Association for Statistical Computing (IASC), Aug 2014, Genève, Switzerland. pp.213-221. ⟨hal-01058278⟩

Partager

Métriques

Consultations de la notice

1063

Téléchargements de fichiers

441