Minor changes in documentation

docs/source/sensitivity/chatterjee.rst

@@ -9,8 +9,9 @@ Consider :math:`n` samples of random variables :math:`X` and :math:`Y`, with :ma
 
    \xi_{n}(X, Y):=1-\frac{3 \sum_{i=1}^{n-1}\left|r_{i+1}-r_{i}\right|}{n^{2}-1}
 
-The Chatterjee index converges for :math:`n \rightarrow \infty` to the Cramér-von Mises index and is faster to estimate than using the Pick and Freeze approach in the Cramér-von Mises index.
+The Chatterjee index converges for :math:`n \rightarrow \infty` to the Cramér-von Mises index and is faster to estimate than using the Pick and Freeze approach to compute the the Cramér-von Mises index.
 
+Furthermore, the Sobol indices can be efficiently estimated by leveraging the same rank statistics, which has the advantage that any sample can be used and no specific pick and freeze scheme is required. 
 
 Chatterjee Class
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

docs/source/sensitivity/sobol.rst

@@ -17,7 +17,7 @@ If the first order index of an input parameter is equal to the total order index
 
 The Sobol indices are typically computed using the Pick-and-Freeze approach for single output and multi-output models. Since there are several variants of the Pick-and-Freeze approach, the schemes implemented to compute Sobol indices are listed below:
 
-Here, :math:`N` is the number of Monte Carlo samples and :math:`m` being the number of input parameters in the model.
+Here, :math:`N` is the Monte Carlo sample size and :math:`m` is the number of input parameters in the model.
 
 1. **First order indices** (:math:`S_{i}`)