The Uncertainty Analysis of Model Results

Provides a step-by-step practical guide to the uncertainty analysis of computer models Discusses the advantages and disadvantages of the suggested methods Points out the benefits of an uncertainty analysis for model robustness and the reliability of the results Explains the difference between aleatory and epistemic uncertainty Includes practical examples

Autorentext

Eduard Hofer holds a Master of Science diploma with distinction in mathematics from the Technical University of Munich (TUM), Germany. He developed a method for the numerical solution of initial value problems with large systems of stiff first-order ordinary differential equations. He also designed a non-commercial, PC-based software system for uncertainty analysis of results from computer models and conducted the uncertainty analysis of numerous applications of computationally demanding computer models. Hofer served on the external peer-review committee of a major US dose reconstruction study with the subtask in uncertainty and sensitivity analysis, and contributed to numerous international conferences. Furthermore, he received an award for his contributions in the field of probabilistic risk assessment.

Inhalt
Preface.- Introduction and necessary distinctions.- Step 1: Search.- Step 2: Quantify.- Step 3: Propagate.- Step 4: Estimate uncertainty.- Step 5: Rank uncertainties.- Step 6: Present the analysis and interpret its results.- Practical execution of the analysis.- Uncertainty analysis when separation of uncertainties is required.- Practical examples.- References.- Subject index.