Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis.
Mit Hilfe einer Sensitivitätsanalyse kann man feststellen, wie ein gegebenes Modell von den Eingabeparametern abhängt und unter welchen Umständen es instabil werden könnte. Die gute Modellierungspraxis kommt ohne solche Betrachtungen zukünftig nicht mehr aus! Ausgehend von einer Erläuterung der Grundprinzipien hilft Ihnen dieses Buch, eine Lösung für Ihre Modellierungsprobleme zu finden. Die Auswirkungen von Unsicherheiten oder Annahmen werden dabei besser durchschaubar.
Inhaltsverzeichnis
What is Sensitivity Analysis.
Hitchhiker's Guide to Sensitivity Analysis.
METHODS.
Designs of Experiments.
Screening Methods.
Local Methods.
Sampling-Based Methods.
Reliability Algorithms: FORM and SORM Methods.
Variance-Based Methods.
Managing the Tyranny of Parameters in Mathematical Modelling of Physical Systems.
Bayesian Sensitivity Analysis.
Graphical Methods.
APPLICATIONS.
Practical Experience in Applying Sensitivity and Uncertainty Analysis.
Scenario and Parametric Sensitivity and Uncertainty Analysis in Nuclear Waste Disposal Risk Assessment: The Case of GESAMAC.
Sensitivity Analysis for Signal Extraction in Economic Time Series.
A Dataless Precalibration Analysis in Solid State Physics.
Appplication of First-Order (FORM) and Second-Order (SORM) Reliability Methods: Analysis and Interpretation of Sensitivity Measures Related to Groundwater Pressure Decreases and Resulting Ground Subsidence.
One-at-a-Time and Mini-Global Analyses for Characterizing Model Sensitivity in the Nonlinear Ozone Predictions from the US EPA Regional Acid Deposition Model (RADM).
Comparing Different Sensitivity Analysis Methods on a Chemical Reactions Model.
An Application of Sensitivity Analysis to Fish Population Dynamics.
Global Sensitivity Analysis: A Quality Assurance Tool in Environmental Policy Modelling.
CONCLUSIONS.
Assuring the Quality of Models Designed for Predictive Tasks.
Fortune and Future of Sensitivity Analysis.
References.
Appendix.
Index.
