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|Title:||Editorial. Special Issue on Sensitivity Analysis of Model Output.|
|Authors:||SCOTT Marian; SALTELLI Andrea|
|Citation:||Journal of Statistical Computation and Simulation (JSCS) vol. 57 p. 1-3|
|Type:||Articles in periodicals and books|
|Abstract:||A very general definition of sensitivity analysis, on which most modellers would agree, is that SA looks at how a given model, generally a computational one, responds to variation in input parameters. There are naturally several aspects of the response of interest. Some typical questions, which SA attempts to answer are: 1) Is there some region in the space of the input parameters for which the model variation is maximum or diverges?; 2) To which parameters is the model most sensitive (locally)?; 3) To which parameters is the model most sensitive (globally)?; 3a) could a simple regression model for the system be realised on a subset of parameters?; 3b) could a subset of parameters be identified which would account for 90% of the variance of my model prediction?; 3c) for system with hundred of parameters: how do i identify the controlling factors with a minimum number of numerical experiments?.|
|JRC Institute:||Joint Research Centre Historical Collection|
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