Cahiers du CEREMADE

Unité Mixte de Recherche du C.N.R.S. N°7534
 
Abstract : An iterative stochastic algorithm to perform maximum a posteriori parameter estimation of hidden Markov models is proposed. It makes the most of the statistical model by introducing an artificial probability model based on an increasing number of the unobserved Markov chain at each iteration. Under minor regularity assumptions, we provide sufficient conditions to ensure global convergence of this algorithm. It is applied to parameter estimation for finite Gaussian mixtures, Markov-modulated Poisson processes and switching autoregressions with a Markov regime.
 
 
MAXIMUM A POSTERIORI PARAMETER ESTIMATION FOR HIDDEN MARKOV MODELS
DOUCET A., ROBERT Christian P.
2000-52
20-12-2000
 
Université de PARIS - DAUPHINE
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