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--- what_is_sensitivity_analysis [2019/11/18 13:34]
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 ====== What is Sensitivity Analysis ======
-Sensitivity analysis (SA) is the study of how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation, and how the given model depends upon the information fed into it Reasons why modellers should carry out a sensitivity analysis are to determin
+Sensitivity analysis (SA) is the study of how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation, and how the given model depends upon the information fed into it. Reasons why modelers should carry out a sensitivity analysis are to determine:
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 //Local and global sensitivity analysis//
-Sensitivity analysis aims at determining how sensitive the model output is to changes in model inputs. When input parameters are relatively certain, we can look at the partial derivative of the output function with respect to the input parameters. This sensitivity measure can easily be computed numerically by performing multiple simulations varying input-parameters around a nominal value. We will find out the local impact of the parameters on the model output and therefore techniques like these are called local sensitivity analysis. For environmental and health risk assessments, input parameters will often be uncertain and therefore local sensitivity analysis techniques will not be usable for a quantitative analysis. We want to find out which of the uncertain input parameters are more important in determining the uncertainty in the output of interest. To find this we need to consider global sensitivity analysis, which are usually implemented using Monte Carlo (MC) simulation and are, therefore, called sampling-based methods.\\
+ aims at determining how sensitive the model output is to changes in model inputs. When input parameters are relatively certain, we can look at the partial derivative of the output function with respect to the input parameters. This sensitivity measure can easily be computed numerically by performing multiple simulations varying input-parameters around a nominal value. We will find out the local impact of the parameters on the model output and therefore techniques like these are called local sensitivity analysis. For environmental and health risk assessments, input parameters will often be uncertain and therefore local sensitivity analysis techniques will not be usable for a quantitative analysis. We want to find out which of the uncertain input parameters are more important in determining the uncertainty in the output of interest. To find this we need to consider global sensitivity analysis, which are usually implemented using Monte Carlo (MC) simulation and are, therefore, called sampling-based methods.\\
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   * [[Sensitivity_analysis_toolbox_wiki|Sensitivity analysis toolbox wiki]]
-  * [[eikos_help.pdf|MATLAB Eikos help document]]
 //References//

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