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Simulated annealing for maximum A Posteriori parameter estimation of hidden Markov models

Abstract:

Hidden Markov models are mixture models in which the populations from one observation to the next are selected according to an unobserved finite state-space Markov chain. Given a realization of the observation process, our aim is to estimate both the parameters of the Markov chain and of the mixture model in a Bayesian framework. In this paper, we present an original simulated annealing algorithm which, in the same way as the EM (Expectation-Maximization) algorithm, relies on data augmentatio...

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Publication status:
Published

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Publisher copy:
10.1109/18.841176

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Publisher:
IEEE
Journal:
IEEE TRANSACTIONS ON INFORMATION THEORY
Volume:
46
Issue:
3
Pages:
994-1004
Publication date:
2000-05-01
DOI:
ISSN:
0018-9448
Source identifiers:
190631
Language:
English
Keywords:
Pubs id:
pubs:190631
UUID:
uuid:3fb92ea0-d883-46bc-b3b3-bc71cca80198
Local pid:
pubs:190631
Deposit date:
2012-12-19

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