Update Missing

docs/source/tutorials/selection_and_stratification.rst

@@ -184,58 +184,58 @@ In this case, the missingness pattern is random and the probability of an entry
 of any missing or non-missing values of other variables in the data-generating process. In other words,
 
 .. math::
-    \Pr(M=0|Y_obs,Y_mis,\psi) = \Pr(M=0|\psi)
+    \Pr(M=0|Y_{obs},Y_{mis},\psi) = \Pr(M=0|\psi)
 
 .. tabs::
 
         .. tab:: Python
-        .. highlight:: python
-        .. code-block:: python
+                .. highlight:: python
+                .. code-block:: python
 
 
-            import dagsim.base as ds
-            import numpy as np
+                    import dagsim.base as ds
+                    import numpy as np
 
 
-            underlying_value = ds.Node(name="underlying_value", function=np.random.normal)
-            index_node = ds.Node(name="index_node", function=np.random.randint, kwargs={"low":0, "high":2})
-            MCAR = ds.Missing(name="MCAR", underlying_value=underlying_value, index_node=index_node)
+                    underlying_value = ds.Node(name="underlying_value", function=np.random.normal)
+                    index_node = ds.Node(name="index_node", function=np.random.randint, kwargs={"low":0, "high":2})
+                    MCAR = ds.Missing(name="MCAR", underlying_value=underlying_value, index_node=index_node)
 
-            list_nodes = [underlying_value, index_node, MCAR]
-            my_graph = ds.Graph(list_nodes=list_nodes, name="MCAR")
+                    list_nodes = [underlying_value, index_node, MCAR]
+                    my_graph = ds.Graph(list_nodes=list_nodes, name="MCAR")
 
-            data = my_graph.simulate(num_samples=10, csv_name="MCAR")
+                    data = my_graph.simulate(num_samples=10, csv_name="MCAR")
 
-        .. tab:: YAML
-        .. highlight:: yaml
-        .. code-block:: yaml
+                .. tab:: YAML
+                .. highlight:: yaml
+                .. code-block:: yaml
 
-            graph:
-              python_file: hello_world_functions.py
-              nodes:
-                underlying_value:
-                  function: numpy.random.normal
-                index_node:
-                  function: numpy.random.randint(0,2)
-                MCAR:
-                  underlying_value: underlying_value
-                  index_node: index_node
+                    graph:
+                      python_file: hello_world_functions.py
+                      nodes:
+                        underlying_value:
+                          function: numpy.random.normal
+                        index_node:
+                          function: numpy.random.randint(0,2)
+                        MCAR:
+                          underlying_value: underlying_value
+                          index_node: index_node
 
 
-            instructions:
-              simulation:
-                csv_name: parser
-                num_samples: 10
+                    instructions:
+                      simulation:
+                        csv_name: parser
+                        num_samples: 10
 
 Missing At Random (MAR)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 In this case, the probability of an entry going missing depends on other observed values in the model, but
 does not depend on any unobserved quantities:
 
 .. math::
-    \Pr(M=0|Y_obs,Y_mis,\psi) = \Pr(M=0|Y_obs,\psi)
+    \Pr(M=0|Y_{obs},Y_{mis},\psi) = \Pr(M=0|Y_{obs},\psi)
 
-In this case, :math:`\Pr(M=0)` depends on the observed value of :math:`Y_obs`.
+In this case, :math:`\Pr(M=0)` depends on the observed value of :math:`Y_{obs}`.
 
 .. code-block:: python
 
@@ -266,10 +266,10 @@ In the MNAR case, the probability that an entry is missing depends not only on o
 so the conditional probability does not simplify:
 
 .. math::
-    \Pr(M=0|Y_obs,Y_mis,\psi) = \Pr(M=0|Y_obs,Y_mis,\psi)
+    \Pr(M=0|Y_{obs},Y_{mis},\psi) = \Pr(M=0|Y_{obs},Y_{mis},\psi)
 
-In this case, :math:`\Pr(M=0)` depends on the observed value of :math:`Y_obs` and the, possibly, unobserved,
-would-have-been value of :math:`Y_mis`.
+In this case, :math:`\Pr(M=0)` depends on the observed value of :math:`Y_{obs}` and the, possibly, unobserved,
+would-have-been value of :math:`Y_{mis}`.
 
 .. code-block:: python