![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > CUED Control Group Seminars > Variational Inference in Gaussian Processes for non-linear time series
Variational Inference in Gaussian Processes for non-linear time seriesAdd 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. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsLMS Invited Lectures 2011 Murray Edwards College Politics Society British Society of Aesthetics Cambridge Lecture SeriesOther talksEmulation for model discrepancy Development of a Broadly-Neutralising Vaccine against Blood-Stage P. falciparum Malaria Electron Catalysis Cosmological Probes of Light Relics Time Reversal Symmetries and the Simulation of Charge Transport in an External Magnetic Field Understanding model diversity in CMIP5 projections of westerly winds over the Southern Ocean |