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Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs.[1][2] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem.
- ^ Saltelli, A.; Ratto, M.; Andreas, T.; Campolongo, F.; Gariboni, J.; Gatelli, D.; Saisana, M.; Tarantola, S. (2008). Global sensitivity analysis: the primer. John Wiley & Sons. doi:10.1002/9780470725184. ISBN 978-0-470-05997-5.
- ^ Saltelli, A.; Tarantola, S.; Campolongo, F.; Ratto, M. (2004). Sensitivity analysis in practice: a guide to assessing scientific models. Vol. 1. doi:10.1002/0470870958. ISBN 978-0-470-87093-8.