Modeling non stationary hidden semi-Markov chains with triplet Markov chains and theory of evidence

Wojciech Pieczynski

doi:10.1109/ssp.2005.1628689

Modeling non stationary hidden semi-Markov chains with triplet Markov chains and theory of evidence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Hidden Markov chains, enabling one to recover the hidden process even for very large size, are widely used in various problems. On the one hand, it has been recently established that when the hidden chain is not stationary, the use of the theory of evidence is equivalent to consider a triplet Markov chain and can improve the efficiency of unsupervised segmentation. On the other hand, hidden semi-Markov chains can also be considered as particular triplet Markov chains. The aim of this paper is to use these two points simultaneously. Considering a non stationary hidden semi-Markov chain, we show that it is possible to consider two auxiliary random chains in such a way that unsupervised segmentation of non stationary hidden semi-Markov chains is workable.

Original language	English
Title of host publication	2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
Publisher	IEEE Computer Society
Pages	727-732
Number of pages	6
ISBN (Print)	0780394046, 9780780394049
DOIs	https://doi.org/10.1109/ssp.2005.1628689
Publication status	Published - 1 Jan 2005
Event	2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Bordeaux, France Duration: 17 Jul 2005 → 20 Jul 2005

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2005

Conference

Conference	2005 IEEE/SP 13th Workshop on Statistical Signal Processing
Country/Territory	France
City	Bordeaux
Period	17/07/05 → 20/07/05

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10.1109/ssp.2005.1628689

Cite this

Pieczynski, W. (2005). Modeling non stationary hidden semi-Markov chains with triplet Markov chains and theory of evidence. In 2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts (pp. 727-732). Article 1628689 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2005). IEEE Computer Society. https://doi.org/10.1109/ssp.2005.1628689

@inproceedings{c5f35493703947448f34ad22a9037232,

title = "Modeling non stationary hidden semi-Markov chains with triplet Markov chains and theory of evidence",

abstract = "Hidden Markov chains, enabling one to recover the hidden process even for very large size, are widely used in various problems. On the one hand, it has been recently established that when the hidden chain is not stationary, the use of the theory of evidence is equivalent to consider a triplet Markov chain and can improve the efficiency of unsupervised segmentation. On the other hand, hidden semi-Markov chains can also be considered as particular triplet Markov chains. The aim of this paper is to use these two points simultaneously. Considering a non stationary hidden semi-Markov chain, we show that it is possible to consider two auxiliary random chains in such a way that unsupervised segmentation of non stationary hidden semi-Markov chains is workable.",

author = "Wojciech Pieczynski",

year = "2005",

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doi = "10.1109/ssp.2005.1628689",

language = "English",

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series = "IEEE Workshop on Statistical Signal Processing Proceedings",

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note = "2005 IEEE/SP 13th Workshop on Statistical Signal Processing ; Conference date: 17-07-2005 Through 20-07-2005",

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Pieczynski, W 2005, Modeling non stationary hidden semi-Markov chains with triplet Markov chains and theory of evidence. in 2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts., 1628689, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2005, IEEE Computer Society, pp. 727-732, 2005 IEEE/SP 13th Workshop on Statistical Signal Processing, Bordeaux, France, 17/07/05. https://doi.org/10.1109/ssp.2005.1628689

Modeling non stationary hidden semi-Markov chains with triplet Markov chains and theory of evidence. / Pieczynski, Wojciech.
2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts. IEEE Computer Society, 2005. p. 727-732 1628689 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Hidden Markov chains, enabling one to recover the hidden process even for very large size, are widely used in various problems. On the one hand, it has been recently established that when the hidden chain is not stationary, the use of the theory of evidence is equivalent to consider a triplet Markov chain and can improve the efficiency of unsupervised segmentation. On the other hand, hidden semi-Markov chains can also be considered as particular triplet Markov chains. The aim of this paper is to use these two points simultaneously. Considering a non stationary hidden semi-Markov chain, we show that it is possible to consider two auxiliary random chains in such a way that unsupervised segmentation of non stationary hidden semi-Markov chains is workable.

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Modeling non stationary hidden semi-Markov chains with triplet Markov chains and theory of evidence

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