Sobolindexen
Sobolindices, also known as Sobol sensitivity indices, are quantitative measures used in global sensitivity analysis to evaluate the contribution of input variables to the variance of a model's output. These indices help identify which parameters most significantly influence model behavior, thereby informing model simplification, uncertainty quantification, and decision-making processes.
The Sobol method decomposes a mathematical model into hierarchical terms based on variance contributions from individual
Sobolindices are particularly valuable in complex models where numerous inputs interact non-linearly or non-additively. They provide
The computation of Sobolindices assumes a model with probabilistic or uncertain inputs, often modeled as random
Overall, Sobolindices serve as essential tools for model analysis, fostering transparency and robustness in computational modeling