Hidden hybrid markov/semi-markov chains
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.
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| Format: | article biblioteca |
| Language: | eng |
| Subjects: | U10 - Informatique, mathématiques et statistiques, F50 - Anatomie et morphologie des plantes, F62 - Physiologie végétale - Croissance et développement, modèle mathématique, modèle de simulation, anatomie végétale, Prunus armeniaca, ramification, floraison, modèle végétal, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_5954, http://aims.fao.org/aos/agrovoc/c_6280, http://aims.fao.org/aos/agrovoc/c_1057, http://aims.fao.org/aos/agrovoc/c_2992, http://aims.fao.org/aos/agrovoc/c_36583, |
| Online Access: | http://agritrop.cirad.fr/526482/ http://agritrop.cirad.fr/526482/1/document_526482.pdf |
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| Summary: | 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. |
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