Risk assessment of modeling predictions is becoming increasingly important as input to decision makers. Probabilistic risk analysis is typically expensive to perform since it generally requires the calculation of a model output Probability Distribution Function (PDF) followed by the integration of the risk portion of the PDF. Here we describe the new risk analysis Guided Monte Carlo (GMC) technique. It maintains the global coverage of Monte Carlo (MC) while judiciously combining model reruns with efficient sensitivity analysis predictions to accurately evaluate the integrated risk portion of the PDF. This GMC technique will facilitate risk analysis of complex models, where the expense was previously prohibitive. Two examples are presented to illustrate the technique, its computational savings and broad applicability. These are an ordinary differential equation based chemical kinetics model and an analytic dosimetry model. For any particular example, the degree of savings will depend on the relative risk being evaluated. In general, the highest fractional degree of savings with the GMC technique will occur for estimating risk levels that are specified in the far wing of the PDF. If no savings are possible, the GMC technique defaults to the true MC limit. In the illustrations presented here, the GMC analysis saved approximately a factor of four in computational effort relative to that of a full MC analysis. Furthermore, the GMC technique can also be implemented with other possible sampling strategies, such as Latin Hypercube, when appropriate.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics
- Monte carlo
- Statistical analysis