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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
