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Asymptotic Properties of Recursive Maximum Likelihood Estimation in State-Space Models

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If you have a question about this talk, please contact Mustapha Amrani.

Advanced Monte Carlo Methods for Complex Inference Problems

Co-author: Arnaud Doucet (University of Oxford)

Recursive maximum likelihood algorithm for state-space models (i.e., for continuous state hidden Markov models) is an iterative estimation method based on particle filter and stochastic gradient search. In this talk, resent results on its asymptotic properties are presented. These results are focused on the asymptotic bias and the asymptotic variance. They also involve diffusion approximation, almost-sure and mean-square convergence of the recursive maximum likelihood algorithm. Some auxiliary (yet, rather interesting) results on the asymptotic properties of the particle filter and the log-likelihood are presented in the talk, too.

This talk is part of the Isaac Newton Institute Seminar Series series.

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