University of Cambridge > Talks.cam > Inference Group > Joint Gaussian Process-Density Mixtures

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Joint Gaussian Process-Density Mixtures

Download to your calendar using vCal

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

Gaussian Processes (GPs) provide a natural framework for Bayesian kernel methods. This talk will be about some work in progress on combining GPs with density estimation in a mixture model. The motivations are: using kernels tuned individually to each mixture component gives a more flexible input-output model, unlabelled data can be used in a semi-supervised setting and the computational complexity can be reduced because only examples belonging to the same mixture component need to be included in the kernel matrix for that mixture component. I will illustrate the idea with a regression example using a coarse two-stage approximation: density estimation followed by weighted GP predictions. A more principled variational Bayes treatment of the joint estimation problem shows how a low complexity solution can be obtained.

This talk is part of the Inference Group series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Department of History and Philosophy of Science https://www.eventbrite.co.uk/myevent?eid=190194656337 Zoom link

Other talks

BSU Seminar: "Response Adaptivity Across Clinical Trials In Portfolios of Biomedical Innovations" A scientists guide to the art of radiation therapy and Neurosurgery for the oncologist New insights on the turbulent energy cascade from non-equilibrium statistical mechanics Fairy Tales Title TBC Polar Oceans Seminar Talk
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity