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SUMMARY:Optimal Uncertainty Quantification - Sullivan\, T (University of W
 arwick)
DTSTART:20130327T152000Z
DTEND:20130327T155000Z
UID:TALK44158@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Uncertainty Quantification lies at the interface of applied ma
 thematics\, probability\, computation and applied sciences\, and has been 
 called ``the end-to-end study of the reliability of scientific inferences.
 ''  It is the understanding of how information (or\nuncertainty) propagate
 s through systems to produce information (or uncertainty) about output qua
 ntities of interest (e.g. structural failure risks or financial portfolio 
 returns)\, and corresponding inverse problems. In many real-world applicat
 ions\, this information propagation spans multiple components or scales an
 d is probabilistic in nature\, but is complicated by non-negligible uncert
 ainty about which probability distributions and models are the ``correct''
  ones. In the Optimal UQ (OUQ) framework\, these problems are formalized a
 s optimization problems over infinite-dimensional feasible sets of probabi
 lity measures and transfer functions.\n
LOCATION:Seminar Room 1\, Newton Institute
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