The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
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.
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Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition
Russell, M., & Moore, R. (1985) · ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1985.1168477 · Cited by 194
Cited by
Yu, S.-Z. (2010) · Artificial Intelligence · doi:10.1016/j.artint.2009.11.011 · Cited by 734
Metadata
- Authors
- Guédon, Y.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 2003-09
- Volume
- 12
- Issue
- 3
- Pages
- 604-639
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W1989844119
- Cited by
- 199
- References
- 2
- FWCI
- 4.59
- Type
- journal article
- Language
- en
- Open access
- closed
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