Kernel-based global sensitivity analysis obtained from a single data set

Results from global sensitivity analysis (GSA) often guide the understanding of complicated input–output systems. Kernel-based GSA methods have recently been proposed for their capability treating a broad scope complex In this paper, we develop new set kernel tools when only single data is available. Three key advances are made: (1) A numerical estimator that demonstrates an empirical improvement over previous procedures. (2) computational method generating inner statistical functions presented. (3) theoretical extension made to define conditional indices, which reveal degree inputs carry shared information about output inherent input–input correlations present. Utilizing these decomposition derived uncertainty based on what called optimal learning sequence input variables, remains consistent exist between variables. While cover range subjects, common solution provided by technique known as mean embedding distributions. The methodology implemented benchmark systems demonstrate insights.