✏️ Change evaluated at to 3rd operand and minor update

Author: CloudChaoszero

docs/source/developer_guide.rst

@@ -134,7 +134,7 @@ below), and x.dist() as the associated density/mass function
 (distribution in the mathematical sense). It is not perfect, and now
 after a few years learning Bayesian statistics I also realized these
 subtleties (i.e., the distinction between *random variable* and
-*distribution*). 
+*distribution*).
 
 But when I was learning probabilistic modelling as a
 beginner, I did find this approach to be the easiest and most
@@ -145,7 +145,7 @@ follows a Gaussian distribution, and
 density function <https://en.wikipedia.org/wiki/Probability_density_function>`__ that takes input :math:`x`
 
 .. math::
-    X:=f(x) = \frac{1}{\sigma \sqrt{(2 \pi)}} * \exp^{- 0.5 * (\frac{x - \mu}{\sigma})^2} = \frac{1}{\sqrt{(2 \pi)}} * \exp^{- 0.5 * x^2}\vert_{\mu = 0, \sigma=1}
+    X:=f(x) = \frac{1}{\sigma \sqrt{2 \pi}} \exp^{- 0.5 (\frac{x - \mu}{\sigma})^2}\vert_{\mu = 0, \sigma=1} = \frac{1}{\sqrt{2 \pi}} \exp^{- 0.5 x^2}
 
 Within a model context, RVs are essentially Theano tensors (more on that
 below). This is different than TFP and pyro, where you need to be more