University of Cambridge > Talks.cam > Statistics > Particle Learning

Particle Learning

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

If you have a question about this talk, please contact rbg24.

This talk will introduce novel particle learning (PL) methods for sequential filtering, parameter learning and smoothing in a general class of state space models. The approach extends existing particle methods by incorporating unknown fixed parameters, utilizing sufficient statistics, for the parameters and/or the states, and allowing for nonlinearities in the model. We also show how to solve the state smoothing problem, integrating out parameter uncertainty. We show that our algorithms outperform MCMC , as well as existing particle filtering algorithms.

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