Simulated annealing for maximum A Posteriori parameter estimation of hidden Markov models - Andrieu, C; Doucet, A (IEEE, 2000)
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 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.