Fixed some parameter subscripts in docs/inference.rst.

docs/inference.rst

@@ -27,35 +27,36 @@ Standardizing
 
 Log dilution is standardized to have mean of zero and standard deviation of one for inference.
 
-Response is left unstandardized to help set priors for `c` and `d`.
+Response is left unstandardized in order to set informative priors for `c` and `d`.
 For instance, it is often known *a priori* that at theoretical minimum or maximum concentrations the response will have some known value.
 
 Priors
 ------
 
-Weakly informative priors are used throughout.
-
+Weakly informative priors are used by default throughout.
 When parameters `a`, `b`, and `c` are fully pooled, they given a prior of :math:`\text{dnorm}(0, 1)`.
-When `a`, `b`, and `c` are partially pooled they are given priors of:
+When `a`, `b`, and `c` are partially pooled across samples, `s`, they are given priors of:
 
 .. math::
 
     \mu_p \sim \text{dnorm}(0, 1)
 
     \sigma_p \sim \text{dexp}(1)
 
-    p_i \sim \text{dnorm}(\mu_p, \sigma_p)
+    p_s \sim \text{dnorm}(\mu_p, \sigma_p)
 
 To prevent redundancy between the values of `c` and `d`, `d` is constrained to be positive.
-When it is fully pooled: :math:`d \sim \text{dexp}(1)`. When it is partially pooled:
+When it is fully pooled: :math:`d \sim \text{dexp}(1)`.
+When it is partially pooled:
 
 .. math::
 
     \sigma_d \sim \text{dexp}(1)
 
-    d_i \sim \text{dnorm}(\sigma_d)
+    d_s \sim \text{dexp}(\sigma_d)
 
 These priors will be applicable for most users.
+
 Partial pooling for `a`, full pooling for `b` and `d`, and setting `c=0` gives the following prior predictive distribution:
 
 .. image:: prior-predictive.png