Mathematical models are used to predict just about everything from traffic and weather to plant metabolism and industrial biotechnology.
However, while they are valuable tools in a broad range of fields, predictive models are still plagued by uncertainties, or errors, and a great deal of effort is directed at determining the extent and effects of these errors.
Now, a team of researchers led by the University of Delaware's Dion Vlachos has developed a framework to address this issue by looking at the effects of correlated parameters.