Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models

Durand Jean-Baptiste, Guédon Yann. 2016. Localizing the latent structure canonical uncertainty: entropy profiles for hidden Markov models. Statistics and Computing, 26 (1) : 549-567.

https://doi.org/10.1007/s11222-014-9494-9

Article de revue ; Article de revue à facteur d'impact

	Version publiée - Anglais Accès réservé aux personnels Cirad Utilisation soumise à autorisation de l'auteur ou du Cirad. 580435.pdf Télécharger (1MB) \| Demander une copie
	Version publiée - Anglais Accès réservé aux personnels Cirad Utilisation soumise à autorisation de l'auteur ou du Cirad. 580435_erratum.pdf Télécharger (332kB) \| Demander une copie

Quartile : Q1, Sujet : STATISTICS & PROBABILITY / Quartile : Q2, Sujet : COMPUTER SCIENCE, THEORY & METHODS

Note générale : Erratum paru dans Statistics and Computing (2016) 26 (1) p. 569-570 http://dx.doi.org/10.1007/s11222-015-9586-1

Résumé : This paper addresses state inference for hidden Markov models. These models rely on unobserved states, which often have a meaningful interpretation. This makes it necessary to develop diagnostic tools for quantification of state uncertainty. The entropy of the state sequence that explains an observed sequence for a given hidden Markov chain model can be considered as the canonical measure of state sequence uncertainty. This canonical measure of state sequence uncertainty is not reflected by the classic multidimensional posterior state (or smoothed) probability profiles because of the marginalization that is intrinsic in the computation of these posterior probabilities. Here, we introduce a new type of profiles that have the following properties: (i) these profiles of conditional entropies are a decomposition of the canonical measure of state sequence uncertainty along the sequence and makes it possible to localise this uncertainty, (ii) these profiles are unidimensional and thus remain easily interpretable on tree structures. We show how to extend the smoothing algorithms for hidden Markov chain and tree models to compute these entropy profiles efficiently. The use of entropy profiles is illustrated by sequence and tree data examples.

Mots-clés Agrovoc : modèle mathématique, méthode statistique, morphologie végétale, croissance, analyse quantitative

Mots-clés libres : Conditional entropy, Hidden Markov chain model, Hidden Markov tree model, Plant structure analysis

Classification Agris : U10 - Informatique, mathématiques et statistiques
F50 - Anatomie et morphologie des plantes
F62 - Physiologie végétale - Croissance et développement

Champ stratégique Cirad : Hors axes (2014-2018)

Auteurs et affiliations