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Overcoming structural uncertainty in computer models
Date:4/1/2014

Philadelphia, PA--A computer model is a representation of the functional relationship between one set of parameters, which forms the model input, and a corresponding set of target parameters, which forms the model output. A true model for a particular problem can rarely be defined with certainty. The most we can do to mitigate error is to quantify the uncertainty in the model.

In a recent paper published in the SIAM/ASA Journal on Uncertainty Quantification, authors Mark Strong and Jeremy Oakley offer a method to incorporate judgments into a model about structural uncertainty that results from building an "incorrect" model.

"Given that 'all models are wrong,' it is important that we develop methods for quantifying our uncertainty in model structure such that we can know when our model is 'good enough'," author Mark Strong says. "Better models mean better decisions."

When making predictions using computer models, we encounter two sources of uncertainty: uncertainty in model inputs and uncertainty in model structure. Input uncertainty arises when we are not certain about input parameters in model simulations. If we are uncertain about true structural relationships within a modelthat is, the relationship between the set of quantities that form the model input and the set that represents the outputthe model is said to display structural uncertainty. Such uncertainty exists even if the model is run using input values as estimated in a perfect study with infinite sample size.

"Perhaps the hardest problem in assessing uncertainty in a computer model prediction is to quantify uncertainty about the model structure, particularly when models are used to predict in the absence of data," says author Jeremy Oakley. "The methodology in this paper can help model users prioritize where improvements are needed in a model to provide more robust support to decision making."

While methods for managing input uncertainty are well
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Contact: Karthika Muthukumaraswamy
karthika@siam.org
267-350-6383
Society for Industrial and Applied Mathematics
Source:Eurekalert  

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