The INI has a new website!

This is a legacy webpage. Please visit the new site to ensure you are seeing up to date information.

Skip to content



Investigating discrepancy in computer model predictions

Oakley, J (University of Sheffield)
Friday 09 September 2011, 10:00-10:30

Seminar Room 1, Newton Institute


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.


[pdf ]


The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.

Back to top ∧