Sensitivity analysis
Sensitivity analysis can be performed on probalistic simulation outputs (see probabilistic simulation). After a probabilistic Monte Carlo simulation, sensitivity indices can be calculated between simulation inputs (parameters) and simulation outputs (blocks).
Sensitivity indices
Method | When to Use |
Pearson product-moment correlation coefficient (Pearson) | Computes the correlation coefficient. Interesting when model is linearly depending on parameters. |
Spearman's rank correlation coefficient (Spearman) | Computes the ranked correlation coefficient. Interesting when model is linearly or monotonically depending on parameters. |
Standardized Regression Coefficient (SRC) | Sets up a regression model from the raw data. Interesting when model is linearly depending on parameters. |
Standardized Rank Regression Coefficient (SRRC) | Sets up a regression model from the ranked data. Interesting when model is linearly or monotonically depending on parameters. |
Partial Correlation Coefficient (PCC) | Computes the correlation coefficient taking into account the rest of the varying parameters. Interesting when model is linearly depending on parameters. |
Partial Rank Correlation Coefficient (PRCC) | Computes the ranked correlation coefficient taking into account the rest of the varying parameters. Interesting when model is linearly or monotonically depending on parameters. |
EASI first order correlation coefficient (EASI) | Quantifies the first order effects, if model is additive this will take into account the full variance. |
XEASI Higher Order Sensitivity Indices(XEASI) | XEASI indices can assist in model calibration and validation processes by revealing which parameters have the most substantial impact on model predictions. |
See also