Being sensitive to uncertainty

被引：20
作者：
Arriola, Leon M. [1 ]
Hyman, James M.
机构：
[1] Univ Wisconsin, Whitewater, WI 53190 USA
[2] Los Alamos Natl Lab, Los Alamos, NM 87545 USA
来源：
COMPUTING IN SCIENCE & ENGINEERING | 2007年 / 9卷 / 02期
关键词：
(Edited Abstract);
D O I：
10.1109/MCSE.2007.27
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：10 / 20
页数：11
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