XEASI (eXtended EASI) Higher Order Sensitivity Indices are a method used in sensitivity analysis, particularly in the context of computer experiments or numerical simulations. Sensitivity analysis aims to understand how variations in input parameters of a model affect the output, thereby identifying which parameters are most influential and how they interact.
XEASI Higher Order Sensitivity Indices extend upon the Elementary Effects method, which is commonly used for estimating sensitivity indices. Elementary Effects involve perturbing one input parameter at a time and observing the changes in the output. Higher Order Sensitivity Indices consider interactions between multiple parameters simultaneously, providing a more comprehensive understanding of the system's behavior.
XEASI Higher Order Sensitivity Indices can help researchers identify not only the main effects of individual parameters but also the interactions between them, including second-order, third-order, and higher-order effects. This information is valuable for understanding complex systems where interactions between parameters may significantly impact the output.
The computation of XEASI Higher Order Sensitivity Indices typically involves conducting a series of model evaluations with different combinations of parameter values, followed by statistical analysis to estimate the sensitivities and interactions. These indices can provide insights into the non-linear behavior of the model and help in making informed decisions about parameter prioritization, model calibration, and uncertainty quantification.
Overall, XEASI Higher Order Sensitivity Indices offer a more nuanced understanding of how input parameters collectively influence the model output, allowing for more robust analysis and decision-making in various fields such as engineering, environmental science, finance, and many others.
