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Predictive modeling's effectiveness is hindered by inherent uncertainties in the input parameters. Sensitivity and uncertainty analysis quantify these uncertainties and identify the relationships between input and output variations, leading to the construction of a more accurate model. This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification
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Leon Arriola
University of Wisconsin–Whitewater
James M. Hyman
Tulane University
Computing in Science & Engineering
Los Alamos National Laboratory
University of Wisconsin–Whitewater
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Arriola et al. (Thu,) studied this question.
synapsesocial.com/papers/6a0ebebba14f152feaf9c86e — DOI: https://doi.org/10.1109/mcse.2007.27