Maximum a Posteriori Parameter Estimation for Hidden Markov Models
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 artiicial probability model based on an increasing number of the unobserved Markov chain at each iteration. Under minor regularity assumptions, we provide suucient conditions to ensure global convergence of this algorithm. It is applied to parameter estimation for nite Gaussian mixtures, Markov-modulated Poisson processes and switching autoregressions with a Markov regime.