Recursive Monte Carlo algorithms for parameter estimation in general state space models
We present new algorithms that aim at estimating the "static" parameters of a latent variable process in an on-line manner. This new class of on-line algorithms is inspired by Monte Carlo Markov chain (MCMC) methods whose use has been mainly restricted to static problems, i.e., for which the set of observations is fixed. The main interest of this new class of algorithms is that it combines MCMC and particle filtering techniques, for which extensive know-how and literature are now available.