University of Cambridge > Talks.cam > Machine Learning @ CUED > Gaussian processes for inferring latent functions in complex data models

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

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

Gaussian processes for inferring latent functions in complex data models

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

If you have a question about this talk, please contact Dr R.E. Turner.

Heavily influenced by the challenges in local volatility modelling from quantitative finance, we consider inferring a latent function in a probabilistic model of data. In vicinity of the former, estimation is approached by deterministic means, commonly least-squares optimisation. Our contribution is to introduce a probabilistic framework based on Gaussian process priors. We approach inference with Markov chain Monte Carlo and extend some of these techniques to scale with data. We propose an approximation that enables sequential sampling of both latent variables and associated hyperparameters. We demonstrate our approach through the local volatility model in a growing data settings which would otherwise be unfeasible with naive, non-sequential sampling.

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

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Spanish Researchers in the United Kingdom (SRUK) AMOP Quantum Journal Club Horizon Forum

Other talks

Cambridge - Corporate Finance Theory Symposium September 2019 Day 1 Paradoxes of multilingualism in public policy in conjunction with the launching of Cambridge Masterclass Series on Multilingualism, Education and Language Policy Babraham Distinguished Lecture - MeCP2 and the causes of Rett syndrome CANCELLED: Kind of Like Making Porn of Yourself:’ Understanding Sexting Through Pornography Art speak Finding the right path: How do multidomain proteins avoid misfolding, and how can women avoid the pitfalls in their scientific career paths?
 

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