Add more to basics doc

docs/basics.rst

@@ -21,10 +21,17 @@ Sensitivity indices come in several forms:
 3. Total-order index: measures the contribution to the output variance caused by
    a model input, including both its first-order effects (the input varying alone)
    and all higher-order interactions.
+
+What is SALib?
+--------------
 
 SALib is an open source library written in Python for performing
-sensitivity analysis.  A typical sensitivity analysis using SALib follows
-four steps:
+sensitivity analysis.  SALib provides a decoupled workflow, meaning it does not
+directly interface with the mathematical or computational model.  Instead,
+SALib is responsible for generating the model inputs, using one of the
+:code:`sample` functions, and computing the sensitivity indices from the model
+outputs, using one of the :code:`analyze` functions.  A typical sensitivity 
+analysis using SALib follows four steps:
 
 1. Determine the model inputs (parameters) and their sample range.  
 
@@ -34,9 +41,12 @@ four steps:
 
 4. Run the :code:`analyze` function on the outputs to compute the sensitivity indices.
 
+SALib provides several sensitivity analysis methods, such as Sobol, Morris,
+and FAST.  There are many factors that determine which method is appropriate
+for a specific application, which we will discuss later.  However, for now, just
+remember that regardless of which method you choose, you need to use only two
+functions: :code:`sample` and :code:`analyze`.
 
 
-SALib is broken into two parts: the *sample generator* and the *sensitivity analysis*.
+.. autofunction:: SALib.analyze.fast.analyze
 
-.. autofunction:: fast.analyze
-    :members: