Hidden hybrid markov/semi-markov chains

Guédon Yann. 2005. Hidden hybrid markov/semi-markov chains. Computational Statistics and Data Analysis, 49 (3) : pp. 663-688.

Journal article ; Article de revue à facteur d'impact
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Abstract : Models that combine Markovian states with implicit geometric state occupancy distributions and semi-Markovian states with explicit state occupancy distributions, are investigated. This type of model retains the flexibility of hidden semi-Markov chains for the modeling of short or medium size homogeneous zones along sequences but also enables the modeling of long zones with Markovian states. The forward-backward algorithm, which in particular enables to implement efficiently the E-step of the EM algorithm, and the Viterbi algorithm for the restoration of the most likely state sequence are derived. It is also shown that macro-states, i.e. series-parallel networks of states with common observation distribution, are not a valid alternative to semi-Markovian states but may be useful at a more macroscopic level to combine Markovian states with semi-Markovian states. This statistical modeling approach is illustrated by the analysis of branching and flowering patterns in plants. (Résumé d'auteur)

Mots-clés Agrovoc : Modèle mathématique, Modèle de simulation, Anatomie végétale, Prunus armeniaca, Ramification, Floraison, Modèle végétal

Mots-clés complémentaires : Algorithme, Architecture végétale, Biomodélisation

Classification Agris : U10 - Computer science, mathematics and statistics
F50 - Plant structure
F62 - Plant physiology - Growth and development

Champ stratégique Cirad : Axe 1 (2005-2013) - Intensification écologique

Auteurs et affiliations

  • Guédon Yann, CIRAD-AMIS-AMAP (FRA)

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