Improve docstring with Gamma function comment

Mention that the continuous approximation is done using n! = Gamma(n+1)

Additionally use :code: and :math: qualifiers on different terms to give
specific highlighting in the rendered docs

pyhf/tensor/mxnet_backend.py

@@ -341,9 +341,10 @@ def einsum(self, subscripts, *operands):
         return self.astensor([])
 
     def poisson(self, n, lam):
-        """
-        The continous approximation to the probability mass function of the Poisson
-        distribution given the parameter `lam` evaluated at `n`.
+        r"""
+        The continous approximation, using :math:`n! = \Gamma\left(n+1\right)`,
+        to the probability mass function of the Poisson distribution evaluated
+        at :code:`n` given the parameter :code:`lam`.
 
         Example:
 
@@ -372,9 +373,10 @@ def poisson(self, n, lam):
         return nd.exp((nd.log(lam) * n) - lam - nd.gammaln(n + 1.))
 
     def normal(self, x, mu, sigma):
-        """
-        The probability density function of the Normal distribution given the parameters
-        evaluated at `x`.
+        r"""
+        The probability density function of the Normal distribution evaluated
+        at :code:`x` given parameters of mean of :code:`mu` and standard deviation
+        of :code:`sigma`.
 
         Example:
 

pyhf/tensor/numpy_backend.py

@@ -160,9 +160,10 @@ def einsum(self, subscripts, *operands):
         return np.einsum(subscripts, *operands)
 
     def poisson(self, n, lam):
-        """
-        The continous approximation to the probability mass function of the Poisson
-        distribution given the parameter `lam` evaluated at `n`.
+        r"""
+        The continous approximation, using :math:`n! = \Gamma\left(n+1\right)`,
+        to the probability mass function of the Poisson distribution evaluated
+        at :code:`n` given the parameter :code:`lam`.
 
         Example:
 
@@ -182,12 +183,13 @@ def poisson(self, n, lam):
         """
         n = np.asarray(n)
         lam = np.asarray(lam)
-        return np.exp((np.log(lam) * n) - lam - gammaln(n + 1.))
+        return np.exp(n * np.log(lam) - lam - gammaln(n + 1.))
 
     def normal(self, x, mu, sigma):
-        """
-        The probability density function of the Normal distribution given parameters
-        of mean of `mu` and standard deviation of `sigma` evaluated at `x`.
+        r"""
+        The probability density function of the Normal distribution evaluated
+        at :code:`x` given parameters of mean of :code:`mu` and standard deviation
+        of :code:`sigma`.
 
         Example:
 

pyhf/tensor/pytorch_backend.py

@@ -171,9 +171,10 @@ def einsum(self, subscripts, *operands):
         return torch.einsum(subscripts, ops)
 
     def poisson(self, n, lam):
-        """
-        The continous approximation to the probability mass function of the Poisson
-        distribution given the parameter `lam` evaluated at `n`.
+        r"""
+        The continous approximation, using :math:`n! = \Gamma\left(n+1\right)`,
+        to the probability mass function of the Poisson distribution evaluated
+        at :code:`n` given the parameter :code:`lam`.
 
         Example:
 
@@ -196,9 +197,10 @@ def poisson(self, n, lam):
         return torch.exp(torch.distributions.Poisson(lam).log_prob(n))
 
     def normal(self, x, mu, sigma):
-        """
-        The probability density function of the Normal distribution given parameters
-        of mean of `mu` and standard deviation of `sigma` evaluated at `x`.
+        r"""
+        The probability density function of the Normal distribution evaluated
+        at :code:`x` given parameters of mean of :code:`mu` and standard deviation
+        of :code:`sigma`.
 
         Example:
 

pyhf/tensor/tensorflow_backend.py

@@ -199,9 +199,10 @@ def einsum(self, subscripts, *operands):
         return tf.einsum(subscripts, *operands)
 
     def poisson(self, n, lam):
-        """
-        The continous approximation to the probability mass function of the Poisson
-        distribution given the parameter `lam` evaluated at `n`.
+        r"""
+        The continous approximation, using :math:`n! = \Gamma\left(n+1\right)`,
+        to the probability mass function of the Poisson distribution evaluated
+        at :code:`n` given the parameter :code:`lam`.
 
         Example:
 
@@ -230,9 +231,10 @@ def poisson(self, n, lam):
         return tf.exp(tf.contrib.distributions.Poisson(lam).log_prob(n))
 
     def normal(self, x, mu, sigma):
-        """
-        The probability density function of the Normal distribution given parameters
-        of mean of `mu` and standard deviation of `sigma` evaluated at `x`.
+        r"""
+        The probability density function of the Normal distribution evaluated
+        at :code:`x` given parameters of mean of :code:`mu` and standard deviation
+        of :code:`sigma`.
 
         Example: