Recursive estimation in mixture models with Markov regime
Ulla Holst, Georg Lindgren · IEEE Transactions on Information Theory · 1991
A recursive algorithm is proposed for estimation of parameters in mixture models, where the observations are governed by a hidden Markov chain. The often badly conditioned information matrix is estimated, and its inverse is incorporated into the algorithm. The performance of the algorithm is studied by simulations of a symmetric normal mixture. The algorithm seems to be stable and produce approximately normally distributed estimates, provided the adaptive matrix is kept well conditioned. Some numerical examples are included.>