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