gemseo / uncertainty / sensitivity / morris

oat module

Class to apply the OAT technique used by MorrisIndices.

OAT technique

The purpose of the One-At-a-Time (OAT) methodology is to quantify the elementary effect

\[df_i = f(X_1+dX_1,\ldots,X_{i-1}+dX_{i-1},X_i+dX_i,\ldots,X_d) - f(X_1+dX_1,\ldots,X_{i-1}+dX_{i-1},X_i,\ldots,X_d)\]

associated with a small variation \(dX_i\) of \(X_i\) with

\[df_1 = f(X_1+dX_1,\ldots,X_d)-f(X_1,\ldots,X_d)\]

The elementary effects \(df_1,\ldots,df_d\) are computed sequentially from an initial point


From these elementary effects, we can compare their absolute values \(|df_1|,\ldots,|df_d|\) and sort \(X_1,\ldots,X_d\) accordingly.