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SUMMARY:Investigating discrepancy in computer model predictions - Oakley\,
  J (University of Sheffield)
DTSTART:20110909T090000Z
DTEND:20110909T093000Z
UID:TALK32737@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:In most computer model predictions\, there will be two sources
  of uncertainty: uncertainty in the choice of model input parameters\, and
  uncertainty in how well the computer model represents reality. Dealing wi
 th the second source of uncertainty can be difficult\, particularly when w
 e have no field data with which to compare the accuracy of the model predi
 ctions. We propose a framework for investigating the "discrepancy" of the 
 computer model output: the difference between the model run at its 'best' 
 inputs and reality\, which involves 'opening the black box' and considerin
 g structural errors within the model. We can then use sensitivity analysis
  tools to identify important sources of model error\, and hence direct eff
 ort into improving the model. Illustrations are given in the field of heal
 th economic modelling.\n
LOCATION:Seminar Room 1\, Newton Institute
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