Estimating hidden semi-Markov chains from discrete sequences.

Abstract : This article addresses the estimation of hidden semi-Markov chains from nonstationary discrete sequences. Hidden semi-Markov chains are particularly useful to model the succession of homogeneous zones or segments along sequences. A discrete hidden semi-Markov chain is composed of a nonobservable state process, which is a semi-Markov chain, and a discrete output process. Hidden semi-Markov chains generalize hidden Markov chains and enable the modeling of various durational structures. From an algorithmic point of view, a new forward-backward algorithm is proposed whose complexity is similar to that of the Viterbi algorithm in terms of sequence length (quadratic in the worst case in time and linear in space). This opens the way to the maximum likelihood estimation of hidden semi-Markov chains from long sequences. This statistical modeling approach is illustrated by the analysis of branching and flowering patterns in plants.
Document type :
Journal articles
Liste complète des métadonnées

Cited literature [27 references]  Display  Hide  Download

https://hal.inria.fr/hal-00826992
Contributor : Christophe Godin <>
Submitted on : Tuesday, May 28, 2013 - 4:47:17 PM
Last modification on : Friday, March 29, 2019 - 9:10:40 AM
Document(s) archivé(s) le : Tuesday, September 3, 2013 - 9:55:09 AM

File

JCGSguedon2003.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00826992, version 1

Citation

Yann Guédon. Estimating hidden semi-Markov chains from discrete sequences.. Journal of Computational and Graphical Statistics, Taylor & Francis, 2003, 12 (3), pp.604-639. ⟨hal-00826992⟩

Share

Metrics

Record views

191

Files downloads

566