Title :
Being Sensitive to Uncertainty
Author :
Arriola, Leon M. ; Hyman, James M.
Author_Institution :
Univ. of Wisconsin-Whitewater, Wisconsin, WI
Abstract :
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
Keywords :
sensitivity analysis; stochastic processes; predictive modeling; sensitivity analysis; uncertainty analysis; Algorithm design and analysis; Computational modeling; Diseases; Input variables; Measurement uncertainty; Predictive models; Probability density function; Sampling methods; Sensitivity analysis; Statistical analysis; analysis; sensitivity; stochastic; uncertainty; volatility;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCSE.2007.27
