An un-summarized change.

scratch.page

@@ -18,6 +18,26 @@ the RSS values of each of the vector elements using $C_i$ as the
 weight. It is calculated in this manner:
 
 $$
-\sigma_{F_s} = \frac{\sum_{i \in F_s} \sigma_i C_i}
+\sigma_{F_s} = \frac{\sum_{i \in F_s} \sigma_i\, C_i}
                     {\sum_{i \in F_s} C_i}
 $$
+# Bayesian Regression
+
+First, specify a set of probabilistic models of the data.
+
+Let a member of this set be denoted by $\mathcal{R}_\alpha$
+
+$\mathcal{R}_\alpha$ has a *prior* probability $P(\mathcal{H}_\alpha)$
+
+On observation of $\mathcal{D}$, the *likelihood* of hypothesis
+$\mathcal{R}_{\alpha}$ is
+$\mathit{P}(\mathcal{D}|\mathcal{R}_{\alpha})$.
+
+The *posterior* probability of $\mathcal{R}_{\alpha}$ is then given by
+$\mathit{P}(\mathcal{H}_{\alpha})\mathit{P}(\mathcal{D}|\mathcal{H}_{\alpha})$
+
+This follows from **Bayes' Theorem** which says
+
+$$
+P(A|B) = \frac{P(B | A)\, P(A)}{P(B)}
+$$