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SUMMARY:Approximate marginalization of uninteresting unknowns in inverse p
 roblems  - Ville Kolehmainen (University of Eastern Finland)
DTSTART:20131024T140000Z
DTEND:20131024T150000Z
UID:TALK46253@talks.cam.ac.uk
CONTACT:Carola-Bibiane Schoenlieb
DESCRIPTION:In the Bayesian inverse problems framework\, all unknown param
 eters are treated as random variables and all uncertainties can be modeled
  systematically. Recently\, the approximation error approach has been prop
 osed for handling modeling errors due to unknown nuisance parameters and m
 odel reduction. In this approach\, approximate marginalization of the mode
 ling errors is carried out before the estimation of the interesting variab
 les. In this talk\, we describe the approximation error approach and prese
 nt computational examples that are related to local X-ray tomography imagi
 ng and electrical impedance tomography.
LOCATION:MR 14\, CMS
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