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Investigating discrepancy in computer model predictions

Friday 9th September 2011 - 10:00 to 10:30
INI Seminar Room 1
Session Title: 
Interpolation and Prediction
Session Chair: 
B. Jones
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 with the second source of uncertainty can be difficult, particularly when we have no field data with which to compare the accuracy of the model predictions. 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 considering structural errors within the model. We can then use sensitivity analysis tools to identify important sources of model error, and hence direct effort into improving the model. Illustrations are given in the field of health economic modelling.
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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons