Step 3. Uncertainty and Sensitivity Analysis

A statistical Post Processor performs the uncertainty and sensitivity analysis of the model outcomes.
The purpose of uncertainty analysis is to determine the uncertainty in estimates for the dependent variables of interest. The purpose of sensitivity analysis is to determine the relationships between the uncertainty in the resultant dependent variables. Uncertainty analysis typically precedes sensitivity analysis since, if the uncertainty in a dependent variable is under an acceptable bound or within an acceptable range, then there is little reason to perform a sensitivity analysis. Further, when a Monte Carlo analysis is being performed, the generation of summary measures such as means, variances and distribution functions used to represent uncertainty requires little effort once the necessary model evaluations have been performed, see references [14].

        uncertainty analysis
        contains the commands necessary to perform the UA

When a Monte Carlo study is being performed, propagation of the sample through the model creates a mapping from analysis inputs to analysis results of the form [ yi, xi1, xi2, ... , xin ],  i = 1,..,m,
where n is the number of independent factors and m is the sample size.
Once this mapping is generated and stored, it can be explored in many ways to determine the sensitivity of model predictions to individual input variables.

        sensitivity analysis
        contains the commands necessary to perform the SA