Nonparametric approach to learning the Bayesian procedure for Hidden Markov Models

The paper reports a series of qualitative attempts ill analyzing tile behaviour of a new learning procedure of the parameters an HMM by modeling different types of stochastic dependencies oil the space of states corresponding to tile underlying finite automaton. The approach aims the development of some new methods in processing image and speech signals in solving pattern recognition problems. Basically, the attempts are stated in terms of weighting processes and deterministic/non deterministic bayesian procedures. The aims were mainly to derive asymptotical conclusions concerning tile performance of the proposed estimation techniques in approximating the ideal bayesian procedure. The proposed methodology adopts tile standard assumptions on the conditional independence properties of the involved stochastic processes.