University of Cambridge > Talks.cam > Statistics > Minimax adaptive estimation in nonparametric Hidden Markov Models

Minimax adaptive estimation in nonparametric Hidden Markov Models

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Quentin Berthet.

In this talk, we review some recents results on nonparametric HMMs. We present and discuss the performances of the spectral estimator and the empirical least squares estimator in the nonparametric framework. In particular, this latter achieves minimax adaptive estimation of the emission laws (i.e. the conditional marginal distributions of the observations given the hidden states).

References :

(with É. Gassiat & C. Lacour) Minimax adaptive estimation of non-parametric Hidden Markov Models, Journal of Machine Learning Research, Volume 17, Issue 111, 2016, Pages 1-43.

(with É. Gassiat & S. Le Corff) Consistent estimation of the filtering and marginal smoothing distributions in nonparametric hidden Markov models

This talk is part of the Statistics series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity