Abstract
We consider the class of stationary nonlinear non Gaussian state space models with unknown static parameters. We propose original online stochastic gradient type algorithms to estimate these parameters. These algorithms rely on the simulation of artificial observations. Contrary to all the methods we are aware of in this framework, optimal state estimation is not required by our methods and the proposed algorithms are computationally efficient. Their efficiency is assessed through simulation.
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