Addressed review comments

Author: lucianopaz

docs/source/Probability_Distributions.rst

@@ -58,16 +58,16 @@ An exponential survival function, where :math:`c=0` denotes failure (or non-surv
     f(c, t) = \left\{ \begin{array}{l} \exp(-\lambda t), \text{if c=1} \\
                \lambda \exp(-\lambda t), \text{if c=0}  \end{array} \right.
 
-Such a function can be implemented as a PyMC distribution by writing a function that specifies the log-probability, then passing that function as a keyword argument to the ``DensityDist`` function, which creates an instance of a PyMC3 distribution with the custom function as its log-probability.
+Such a function can be implemented as a PyMC distribution by writing a function that specifies the log-probability, then passing that function as a keyword argument to the ``DensityDist`` function, which creates an instance of a PyMC distribution with the custom function as its log-probability.
 
 For the exponential survival function, this is:
 
 ::
 
-    def logp(value, t, λ):
-        return (value * log(λ) - λ * t).sum()
+    def logp(value, t, lam):
+        return (value * log(lam) - lam * t).sum()
 
-    exp_surv = pm.DensityDist('exp_surv', t, λ, logp=logp, observed=failure)
+    exp_surv = pm.DensityDist('exp_surv', t, lam, logp=logp, observed=failure)
 
 Similarly, if a random number generator is required, a function returning random numbers corresponding to the probability distribution can be passed as the ``random`` argument.
 

pymc/distributions/distribution.py

@@ -434,7 +434,7 @@ def __new__(
         name : str
         dist_params : Tuple
             A sequence of the distribution's parameter. These will be converted into
-            aesara tensors internally. These parameters could be other ``RandomVariable``
+            Aesara tensors internally. These parameters could be other ``RandomVariable``
             instances.
         logp : Optional[Callable]
             A callable that calculates the log density of some given observed ``value``

pymc/tests/test_moment.py

@@ -10,7 +10,7 @@
 from pymc.distributions.shape_utils import to_tuple
 
 
-@pytest.mark.parametrize("size", [None, (), (2,), (3, 2)], ids=str)
+@pytest.mark.parametrize("size", [(), (2,), (3, 2)], ids=str)
 def test_density_dist_moment_scalar(size):
     def moment(rv, size, mu):
         return (at.ones(size) * mu).astype(rv.dtype)
@@ -24,7 +24,7 @@ def moment(rv, size, mu):
     assert np.all(evaled_moment == mu_val)
 
 
-@pytest.mark.parametrize("size", [None, (), (2,), (3, 2)], ids=str)
+@pytest.mark.parametrize("size", [(), (2,), (3, 2)], ids=str)
 def test_density_dist_moment_multivariate(size):
     def moment(rv, size, mu):
         return (at.ones(size)[..., None] * mu).astype(rv.dtype)