University of Cambridge > Talks.cam > Machine Learning @ CUED > Variational inference for partially observed diffusion processes

  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

Variational inference for partially observed diffusion processes

Download to your calendar using vCal

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

Variational inference is an approximate inference scheme popular in Machine Learning. In contrast to MCMC it leads to a deterministic solution, is relatively quick to converge and its convergence can easily be checked by monitoring the evolution of the variational bound. In this talk we will recall the basic concepts underlying variational EM and show how this framework can be extended to continuous-time stochastic processes. More specifically, we will apply the variational approach to partially observed diffusion processes and discuss parameter inference in this context. I will also discuss the links with statistical linearisation and sigma-point transformations.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Stem Cell Yusuf Hamied Theatre Christ's College Cambridge Lucia Windsor Room (Newnham College)

Other talks

Polar Oceans Seminar Talk Bigger Picture Talk: Developing future marine engines running on ammonia and other green fuels In conversation: Louis Herns Marcelin and Ola Osman on legacies of enslavement and contemporary violence in Liberia and Haiti Caribbean Trip and Cacti Animating urbanism: the demotic multiplicities of seeing cities digitally Differences between Robin and Neumann eigenvalues
 

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