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SUMMARY:Bayesian Quadrature for Multiple Related Integrals - Francois-Xavi
 er Briol (Imperial College London\; University of Warwick\; University of 
 Oxford)
DTSTART:20180221T110000Z
DTEND:20180221T130000Z
UID:TALK101251@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Bayesian probabilistic numerical methods are a set of tools pr
 oviding posterior distributions on the output of numerical methods. The us
 e of these methods is usually motivated by the fact that they can represen
 t our uncertainty due to incomplete/finite information about the continuou
 s mathematical problem being approximated. In this talk\, we demonstrate t
 hat this paradigm can provide additional advantages\, such as the possibil
 ity of transferring information between several numerical methods. This al
 lows users to represent uncertainty in a more faithfully manner and\, as a
  by-product\, provide increased numerical efficiency. We propose the first
  such numerical method by extending the well-known Bayesian quadrature alg
 orithm to the case where we are interested in computing the integral of se
 veral related functions. We then demonstrate its efficiency in the context
  of multi-fidelity models for complex engineering systems\, as well as a p
 roblem of global illumination in computer graphics.<br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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