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SUMMARY:Bayesian model calibration for generalized linear models: An appli
 cation in radiation transport - Derek Bingham (Simon Fraser University)
DTSTART:20180412T123000Z
DTEND:20180412T133000Z
UID:TALK103729@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Co-author: Mike Grosskopf (Los Alamos National Lab)<span><br><
 br>Model calibration uses outputs from a simulator and fi&#12\;eld data to
  build a predictive model for the physical system and to estimate unknown 
 inputs. The conventional approach to model calibration assumes that the ob
 servations are continuous outcomes. In many applications this is not the c
 ase. The methodology proposed was motivated by an application in modeling 
 photon counts at the Center for Exascale Radiation Transport. There\, high
  performance computing is used for simulating the flow of neutrons through
  various materials. In this talk\, new Bayesian methodology for computer m
 odel calibration to handle the count structure of our observed data allows
  closer &#12\;fidelity to the experimental system and provides flexibility
  for identifying different forms of model discrepancy between the simulato
 r and experiment.</span><br>
LOCATION:Seminar Room 1\, Newton Institute
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