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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Deep Gaussian Process Priors for Bayesian Inverse
Problems - Aretha Teckentrup (University of Edinb
urgh)
DTSTART;TZID=Europe/London:20180412T113000
DTEND;TZID=Europe/London:20180412T120000
UID:TALK103723AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/103723
DESCRIPTION:Co-authors: Matt Dunlop (Caltech)\, Mark Girolami
(Imperial College)\, Andrew Stuart (Caltech)
Deep Gaussian processes have received a great de
al of attention in the last couple of years\, due
to their ability to model very complex behaviour.
In this talk\, we present a general framework for
constructing deep Gaussian processes\, and provide
a mathematical argument for why the depth of the
processes is in most cases finite. We also present
some numerical experiments\, where deep Gaussian
processes have been employed as prior distribution
s in Bayesian inverse problems.
Related Lin
ks
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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