Once scenarios have been defined, a calculational methodology for evaluating their consequences must be developed. The calculational methodology must address stochastic uncertainty related to aggregation and stochastic variation, and subjective uncertainty because of, for example, measurement difficulties or unknowable data. The inner sum denoted with the subscript st is a probabilistic characterization of the uncertainty associated with parameters used to characterize stochastic uncertainty. For example, the stochastic uncertainty may be the uncertainty in the number and time of human intrusion boreholes. The outer sum denoted with the subscript su is a probabilistic characterization of the uncertainty associated with parameters used to characterize subjective uncertainty.
Subjective uncertainty results from measurement uncertainty or unknowable data.
Techniques for Probabilistic Analysis Monte Carlo analysis is the general name for the probabilistic techniques used to evaluate the double sum discussed above. Within the general framework of Monte Carlo analysis, PA uses two methods for generating the samples propagated through the model system. Information about the ranges and distributions of possible input parameter values can be drawn from a variety of sources, including field data, laboratory data, and literature. A review process leads from the available data to the construction of the distribution functions used in the performance assessment to characterize uncertainty in input parameters. This sampling is used for parameters evaluated in the inner sum of the double sum.