Hidden hybrid Markov/semi-Markov chains. - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Computational Statistics and Data Analysis Year : 2005

Hidden hybrid Markov/semi-Markov chains.

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.
Fichier principal
Vignette du fichier
CSDAguedon2005.pdf (566.13 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00830074 , version 1 (04-06-2013)

Identifiers

Cite

Yann Guédon. Hidden hybrid Markov/semi-Markov chains.. Computational Statistics and Data Analysis, 2005, 49 (3), pp.663-688. ⟨10.1016/j.csda.2004.05.033⟩. ⟨hal-00830074⟩
192 View
473 Download

Altmetric

Share

Gmail Facebook X LinkedIn More