Update notation

\alpha -> a
\beta -> b

courses/stat615/slides/ABC/ABC.Rnw

@@ -269,11 +269,11 @@ ggplot(melt(d, id.var='time', variable.name='species', value.name='count'),
 	\[ \hat{\theta}_j = \frac{n_j}{I_j} \]
 	\item \pause Conjugate Bayesian inference \pause
 	\[ \begin{array}{ll}
-	p(\theta) &= \prod_{j=1}^M Ga(\theta_j; \alpha_j,\beta_j) \\
+	p(\theta) &= \prod_{j=1}^M Ga(\theta_j; a_j,b_j) \\
 	\pause \\
-	p(\theta|X) &= \prod_{j=1}^M Ga(\theta_j; \alpha_j+n_j, \beta_j+I_j) \\
+	p(\theta|X) &= \prod_{j=1}^M Ga(\theta_j; a_j+n_j, b_j+I_j) \\
 	\pause \\
-	E[\theta_j|X] &= \frac{\alpha_j+n_j}{\beta_j+I_j}
+	E[\theta_j|X] &= \frac{a_j+n_j}{b_j+I_j}
 	\end{array} \]
 	\end{itemize}
 \end{frame}
@@ -380,7 +380,7 @@ g + xlim(30,31)
 \frametitle{Summary}
 	\begin{itemize}
 	\item	With complete observations and independent gamma priors, the posterior is
-	\[ p(\theta|X) = \prod_{j=1}^M Ga(\theta_j; \alpha_j+n_j, \beta_j+I_j) \]
+	\[ p(\theta|X) = \prod_{j=1}^M Ga(\theta_j; a_j+n_j, b_j+I_j) \]
 	where 
 	\[ \begin{array}{ll}
 	n_j &= \sum_{i=1}^n \mathrm{I}(r_i=j) \\