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Being Sensitive to Uncertainty
March/April 2007 (vol. 9 no. 2)
pp. 10-20
Leon M. Arriola, University of Wisconsin?Whitewater
James M. Hyman, Los Alamos National Laboratory
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.
Index Terms:
stochastic, sensitivity, uncertainty, analysis, volatility
Citation:
Leon M. Arriola, James M. Hyman, "Being Sensitive to Uncertainty," Computing in Science and Engineering, vol. 9, no. 2, pp. 10-20, March-April 2007, doi:10.1109/MCSE.2007.27
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