updated docstring for pm.MatrixNormal-after review

Author: Rojan Shrestha

pymc/distributions/multivariate.py

@@ -1792,7 +1792,7 @@ class MatrixNormal(Continuous):
     --------
     Define a matrixvariate normal variable for given row and column covariance
     matrices::
-
+    with pm.Model() as model:
         colcov = np.array([[1.0, 0.5], [0.5, 2]])
         rowcov = np.array([[1, 0, 0], [0, 4, 0], [0, 0, 16]])
         m = rowcov.shape[0]
@@ -1811,29 +1811,29 @@ class MatrixNormal(Continuous):
 
     .. code:: python
 
-            # Setup data
-            true_colcov = np.array([[1.0, 0.5, 0.1],
-                                    [0.5, 1.0, 0.2],
-                                    [0.1, 0.2, 1.0]])
-            m = 3
-            n = true_colcov.shape[0]
-            true_scale = 3
-            true_rowcov = np.diag([true_scale**(2*i) for i in range(m)])
-            mu = np.zeros((m, n))
-            true_kron = np.kron(true_rowcov, true_colcov)
-            data = np.random.multivariate_normal(mu.flatten(), true_kron)
-            data = data.reshape(m, n)
-
-            with pm.Model() as model:
-                # Setup right cholesky matrix
-                sd_dist = pm.HalfCauchy.dist(beta=2.5, shape=3)
-                colchol,_,_ = pm.LKJCholeskyCov('colchol', n=3, eta=2,sd_dist=sd_dist)
-                # Setup left covariance matrix
-                scale = pm.LogNormal('scale', mu=np.log(true_scale), sigma=0.5)
-                rowcov = pt.diag([scale**(2*i) for i in range(m)])
-
-                vals = pm.MatrixNormal('vals', mu=mu, colchol=colchol, rowcov=rowcov,
-                                    observed=data)
+        # Setup data
+        true_colcov = np.array([[1.0, 0.5, 0.1],
+                                [0.5, 1.0, 0.2],
+                                [0.1, 0.2, 1.0]])
+        m = 3
+        n = true_colcov.shape[0]
+        true_scale = 3
+        true_rowcov = np.diag([true_scale**(2*i) for i in range(m)])
+        mu = np.zeros((m, n))
+        true_kron = np.kron(true_rowcov, true_colcov)
+        data = np.random.multivariate_normal(mu.flatten(), true_kron)
+        data = data.reshape(m, n)
+
+        with pm.Model() as model:
+            # Setup right cholesky matrix
+            sd_dist = pm.HalfCauchy.dist(beta=2.5, shape=3)
+            colchol,_,_ = pm.LKJCholeskyCov('colchol', n=3, eta=2,sd_dist=sd_dist)
+            # Setup left covariance matrix
+            scale = pm.LogNormal('scale', mu=np.log(true_scale), sigma=0.5)
+            rowcov = pt.diag([scale**(2*i) for i in range(m)])
+
+            vals = pm.MatrixNormal('vals', mu=mu, colchol=colchol, rowcov=rowcov,
+                                observed=data)
     """
 
     rv_op = matrixnormal