Implemented feedback from John and Shilpa

Author: slilonfe5

doc/OnlineDocs/explanation/analysis/doe/guide.rst

@@ -16,7 +16,7 @@ MBDoE analysis. This is in line with the convention used in the parameter estima
 
 An example of the subclassed :class:`Experiment` object that builds and labels the model is shown in the next few sections.
 
-This guide illustrates the use of Pyomo.DoE using a reaction kinetics example (Wang and Dowling, 2022).
+This guide illustrates the use of Pyomo.DoE using a reaction kinetics example ([WD22]_).
 
 .. math::
    :nowrap:
@@ -117,17 +117,17 @@ Step 5: Exploratory analysis (Enumeration)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 After creating the subclass of the :class:`Experiment` class, exploratory analysis is
-suggested to enumerate the design space to check if the problem is identifiable,
-i.e., ensure that D-, E-optimality metrics are not small numbers near zero, and
-Modified E-optimality is not a big number.
+suggested to systematically enumerate the experimental design space and identify regions
+that provide high information content about the model parameters, as quantified by
+the A-, D-, E-, and ME-optimality criteria.
 Additionally, it helps to initialize the model for the optimal experimental design step.
 
-Pyomo.DoE can perform exploratory sensitivity analysis with the ``compute_FIM_full_factorial`` method.
-The ``compute_FIM_full_factorial`` method generates a grid over the design space as specified by the user.
-Each grid point represents an MBDoE problem solved using the ``compute_FIM`` method.
+Pyomo.DoE can perform exploratory sensitivity analysis with the :meth:`compute_FIM_full_factorial` method.
+The :meth:`compute_FIM_full_factorial` method generates a grid over the design space as specified by the user.
+Each grid point represents an MBDoE problem solved using the :meth:`compute_FIM` method.
 In this way, sensitivity of the FIM over the design space can be evaluated.
-Pyomo.DoE supports plotting the results from the ``compute_FIM_full_factorial`` method
-with the ``draw_factorial_figure`` method.
+Pyomo.DoE supports plotting the results from the :meth:`compute_FIM_full_factorial` method
+with the :meth:`draw_factorial_figure` method.
 
 The following code defines the ``run_reactor_doe`` function. This function encapsulates
 the workflow for both sensitivity analysis (Step 5) and optimal design (Step 6).

doc/OnlineDocs/explanation/analysis/doe/overview.rst

@@ -127,4 +127,4 @@ We can use the FIM or the covariance matrix to define the design criteria.
     A confidence ellipse is a geometric representation of the uncertainty in parameter
     estimates. It is derived from the covariance matrix :math:`\mathbf{V}`.
 
-In order to solve problems of the above, Pyomo.DoE implements the 2-stage stochastic program. Please see Wang and Dowling (2022) for details.
+In order to solve problems of the above, Pyomo.DoE implements the 2-stage stochastic program. Please see [WD22]_ for details.

doc/OnlineDocs/reference/bibliography.rst

@@ -179,3 +179,7 @@ Bibliography
 
 .. [NW99] Nocedal, Jorge, and Stephen J. Wright, eds. Numerical
    optimization. New York, NY: Springer New York, 1999.
+
+.. [WD22] Wang, Jialu, and Alexander W. Dowling. "Pyomo.DOE: An open‐source
+   package for model‐based design of experiments in Python." *AIChE Journal*,
+   68(12), e17813. 2022. DOI `10.1002/aic.17813Y <https://doi.org/10.1002/aic.17813Y>`_