...

paper/imputation.tex

@@ -209,10 +209,7 @@ Formally, we obtain
 \begin{equation}
 	\prob{\parameters | \dataset} = \frac{\prob{\dataset | \parameters} \prob{\parameters}}{\prob{\dataset}} .
 \end{equation}
-%
-In practice, we use the MCMC functionality of Stan\footnote{\url{https://mc-stan.org/}} to obtain a sample \sample of this posterior distribution, where each element of \sample contains one instance of parameters \parameters.
-%
-Sample \sample can now be used to compute various probabilistic quantities of interest, including a (posterior) distribution of \unobservable for each entry in dataset \dataset.
+
 
 \spara{Computing counterfactuals} 
 Having obtained a posterior probability distribution for parameters \parameters in parameter space \parameterSpace, we can now expand expression~(\ref{eq:counterfactual}) as follows.
@@ -256,6 +253,12 @@ Having obtained outcome estimates for data entries with $\decision_\human = 0$ a
 %
 Our approach is summarized in Figure~\ref{fig:approach}.
 
+\spara{Implementation} 
+%
+In practice, we use the MCMC functionality of Stan\footnote{\url{https://mc-stan.org/}} to obtain a sample \sample of this posterior distribution, where each element of \sample contains one instance of parameters \parameters.
+%
+Sample \sample can now be used to compute various probabilistic quantities of interest, including a (posterior) distribution of \unobservable for each entry in dataset \dataset.
+
 %Original by Michael and Riku
 %\subsection{Model definition} \label{sec:model_definition}
 %