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Journal article

Learning interaction dynamics with coupled hidden Markov models

Abstract:

An analysis of interactions between different physiological control systems may only be possible with correlation functions if the signals have similar spectral distributions. Interactions between such signals can be modelled in state space rather than observation space, i.e. interactions are modelled after first translating the observations into a common domain. Coupled hidden Markov models (CHMM) are such state-space models. They form a natural extension to standard hidden Markov models. Th...

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Publication status:
Published

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Publisher copy:
10.1049/ip-smt:20000851

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
IEE
Journal:
IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY
Volume:
147
Issue:
6
Pages:
345-350
Publication date:
2000-11-01
DOI:
EISSN:
1359-7094
ISSN:
1350-2344
Source identifiers:
62991
Language:
English
Pubs id:
pubs:62991
UUID:
uuid:5b3eb70c-982c-4e93-862e-cf37fa99c106
Local pid:
pubs:62991
Deposit date:
2012-12-19

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