University of Cambridge > > CUED Control Group Seminars > Variational Inference in Gaussian Processes for non-linear time series

Variational Inference in Gaussian Processes for non-linear time series

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

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

Modelling non-linear dynamical systems from observations (system identification) is often a pre-requisite for designing good controllers. Despite the fundamental importance of this problem, good models and inference methods remain a technical challenge. The difficulties include having to deal with noisy and incomplete measurements and treating suitably flexible non-linear models. Sufficiently flexible models generally won’t have succinct representations, and large numbers of free parameters requires principled approaches to issues such as overfitting. In this talk I will present a couple of recent developments in machine learning which together allows approximate fully Bayesian inference jointly over both latent states and non-linear transition models. This elegant and practical framework relies on variational inference in non-linear Gaussian process state space models.

This talk is part of the CUED Control Group Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity