![]() ![]() We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default they hold all other parameters fixed at baseline values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values.
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