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Simulated annealing for maximum a posteriori parameter estimation of hidden Markov models

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2 Author(s)
Andrieu, C. ; Dept. of Eng., Cambridge Univ., UK ; Doucet, Arnaud

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. We present an original simulated annealing algorithm which, in the same way as the EM (expectation-maximization) algorithm, relies on data augmentation, and is based on stochastic simulation of the hidden Markov chain. This algorithm is shown to converge toward the set of maximum a posteriori (MAP) parameters under suitable regularity conditions

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Information Theory, IEEE Transactions on  (Volume:46 ,  Issue: 3 )