fix up LaTeX in docstrings

Author: drbenvincent

causalpy/pymc_models.py

@@ -190,13 +190,10 @@ class LinearRegression(PyMCModel):
     Defines the PyMC model
 
     .. math::
-        \beta &\sim \mathrm{Normal}(0, 50)
-
-        \sigma &\sim \mathrm{HalfNormal}(1)
-
-        \mu &= X * \beta
-
-        y &\sim \mathrm{Normal}(\mu, \sigma)
+        \beta &\sim \mathrm{Normal}(0, 50) \\
+        \sigma &\sim \mathrm{HalfNormal}(1) \\
+        \mu &= X \cdot \beta \\
+        y &\sim \mathrm{Normal}(\mu, \sigma) \\
 
     Example
     --------
@@ -236,14 +233,10 @@ class WeightedSumFitter(PyMCModel):
     Defines the PyMC model:
 
     .. math::
-
-        \sigma &\sim \mathrm{HalfNormal}(1)
-
-        \beta &\sim \mathrm{Dirichlet}(1,...,1)
-
-        \mu &= X * \beta
-
-        y &\sim \mathrm{Normal}(\mu, \sigma)
+        \sigma &\sim \mathrm{HalfNormal}(1) \\
+        \beta &\sim \mathrm{Dirichlet}(1,...,1) \\
+        \mu &= X \cdot \beta \\
+        y &\sim \mathrm{Normal}(\mu, \sigma) \\
 
     Example
     --------
@@ -433,14 +426,10 @@ class PropensityScore(PyMCModel):
     Defines the PyMC model
 
     .. math::
-        \beta &\sim \mathrm{Normal}(0, 1)
-
-        \sigma &\sim \mathrm{HalfNormal}(1)
-
-        \mu &= X * \beta
-
-        p &= logit^{-1}(mu)
-
+        \beta &\sim \mathrm{Normal}(0, 1) \\
+        \sigma &\sim \mathrm{HalfNormal}(1) \\
+        \mu &= X \cdot \beta \\
+        p &= \text{logit}^{-1}(\mu) \\
         t &\sim \mathrm{Bernoulli}(p)
 
     Example