@@ -66,7 +66,7 @@ Bayesian reconstruction algorithms may be viewed in terms of three basic buildin

It contains the data, the statistics of the data including the error bars and a description of the measurement device itself.

\item The \emph{prior}$\mathcal P(s)$ describes the knowledge the scientist has \emph{before} executing the experiment.

In order to define a prior one needs to make one's knowledge about the physical process which was observed by the measurement device explicit.

\item Finally, one needs an algorithmic and computational framework which is able to actually do the Bayesian inference, compute the \emph{posterior}$\mathcal P(s/d)$, given the above information.

\item Finally, one needs an algorithmic and computational framework which is able to actually do the Bayesian inference, compute the \emph{posterior}$\mathcal P(s|d)$, given the above information.

\end{enumerate}

It becomes clear that these three parts are separate from each other.