diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
index f76a98546e..142ee4fcd8 100644
--- a/pymc/distributions/multivariate.py
+++ b/pymc/distributions/multivariate.py
@@ -2705,31 +2705,31 @@ class ZeroSumNormal(Distribution):
 
     .. math::
 
-        \begin{align*}
-            ZSN(\sigma) = N \Big( 0, \sigma^2 (I - \tfrac{1}{n}J) \Big) \\
-            \text{where} \ ~ J_{ij} = 1 \ ~ \text{and} \\
-            n = \text{nbr of zero-sum axes}
-        \end{align*}
+    \begin{align*}
+    ZSN(\sigma) = N \Big( 0, \sigma^2 (I_K - \tfrac{1}{K}J_K) \Big) \\
+    \text{where} \ ~ J_{ij} = 1 \ ~ \text{and} \\
+    K = \text{size (length) of the constrained axis}
+    \end{align*}
 
     Parameters
     ----------
     sigma : tensor_like of float
-        Scale parameter (sigma > 0).
-        It's actually the standard deviation of the underlying, unconstrained Normal distribution.
-        Defaults to 1 if not specified. ``sigma`` cannot have length > 1 across the zero-sum axes.
+    Scale parameter (sigma > 0).
+    It's actually the standard deviation of the underlying, unconstrained Normal distribution.
+    Defaults to 1 if not specified. ``sigma`` cannot have length > 1 across the zero-sum axes.
     n_zerosum_axes: int, defaults to 1
-        Number of axes along which the zero-sum constraint is enforced, starting from the rightmost position.
-        Defaults to 1, i.e the rightmost axis.
+    Number of axes along which the zero-sum constraint is enforced, starting from the rightmost position.
+    Defaults to 1, i.e the rightmost axis.
     dims: sequence of strings, optional
-        Dimension names of the distribution. Works the same as for other PyMC distributions.
-        Necessary if ``shape`` is not passed.
+    Dimension names of the distribution. Works the same as for other PyMC distributions.
+    Necessary if ``shape`` is not passed.
     shape: tuple of integers, optional
-        Shape of the distribution. Works the same as for other PyMC distributions.
-        Necessary if ``dims`` or ``observed`` is not passed.
+    Shape of the distribution. Works the same as for other PyMC distributions.
+    Necessary if ``dims`` or ``observed`` is not passed.
 
     Warnings
     --------
-    Currently, ``sigma``cannot have length > 1 across the zero-sum axes to ensure the zero-sum constraint.
+    Currently, ``sigma`` cannot have length > 1 across the zero-sum axes to ensure the zero-sum constraint.
 
     ``n_zerosum_axes`` has to be > 0. If you want the behavior of ``n_zerosum_axes = 0``,
     just use ``pm.Normal``.
@@ -2737,23 +2737,23 @@ class ZeroSumNormal(Distribution):
     Examples
     --------
     Define a `ZeroSumNormal` variable, with `sigma=1` and
-    `n_zerosum_axes=1`  by default::
-
-        COORDS = {
-            "regions": ["a", "b", "c"],
-            "answers": ["yes", "no", "whatever", "don't understand question"],
-        }
-        with pm.Model(coords=COORDS) as m:
-            # the zero sum axis will be 'answers'
-            v = pm.ZeroSumNormal("v", dims=("regions", "answers"))
-
-        with pm.Model(coords=COORDS) as m:
-            # the zero sum axes will be 'answers' and 'regions'
-            v = pm.ZeroSumNormal("v", dims=("regions", "answers"), n_zerosum_axes=2)
-
-        with pm.Model(coords=COORDS) as m:
-            # the zero sum axes will be the last two
-            v = pm.ZeroSumNormal("v", shape=(3, 4, 5), n_zerosum_axes=2)
+    `n_zerosum_axes=1` by default::
+
+    COORDS = {
+    "regions": ["a", "b", "c"],
+    "answers": ["yes", "no", "whatever", "don't understand question"],
+    }
+    with pm.Model(coords=COORDS) as m:
+        # the zero sum axis will be 'answers'
+        v = pm.ZeroSumNormal("v", dims=("regions", "answers"))
+
+    with pm.Model(coords=COORDS) as m:
+        # the zero sum axes will be 'answers' and 'regions'
+        v = pm.ZeroSumNormal("v", dims=("regions", "answers"), n_zerosum_axes=2)
+
+    with pm.Model(coords=COORDS) as m:
+        # the zero sum axes will be the last two
+        v = pm.ZeroSumNormal("v", shape=(3, 4, 5), n_zerosum_axes=2)
     """
 
     rv_type = ZeroSumNormalRV