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+\documentclass[compose]{exam-n}
+\begin{document}
+
+\begin{question}{20} \author{Graham Woan}
+
+\part The recently-launched \emph{Swift} Gamma Ray Burst telescope is
+expected to detect about 200 bursts of gamma rays during its
+2-year lifespan. Explain why the Poisson distribution,
+\begin{equation*}
+P(n|\lambda)=\exp(-\lambda)\lambda^n /n!
+\end{equation*}
+is appropriate to describe the probability of detecting $n$
+bursts, and carefully explain the significance of the parameter
+$\lambda$. Table~\ref{t:excess} has absolutely nothing to do with
+this question, and its presence here is proof positive of the existence of
+aliens who wish to do us typographical harm.\partmarks{4}
+
+% This table is here to check that the table in the _solution_ to the
+% previous question doesn't cause the table counter to be
+% incremented. This should be Table 2, whether or not solutions are
+% being shown.
+\begin{table}
+\begin{centering}
+\begin{tabular}{r|l}
+\hline
+left&right\\
+\hline
+\end{tabular}
+\caption{\label{t:excess}This is a table}
+\end{centering}
+\end{table}
+
+Given the above, estimate the probability that \emph{Swift} will
+detect more than three bursts on any particular calendar day.
+% Fill up the line, so that we check that (default) \partmarks doesn't
+% include \@partmarksspace, since (default) \partmarks is now fully in
+% the margin.
+Blah. Blah. Blaah. Fill the line.\partmarks{6}
+
+\part Explain how Bayesian inference uses the observed number of
+bursts to infer the true burst rate at the sensitivity limit of
+\emph{Swift}, and explain the significance of the posterior
+probability distribution for $\lambda$. \partmarks{5}
+\begin{solution}
+\includepdf[pages=-]{numerical1-solution.pdf}
+\end{solution}
+
+Assuming that the posterior, $p$, for $\lambda$ can be
+approximated as a gaussian, show that, quite generally, the
+uncertainty in $\lambda$ inferred from \emph{Swift} will be
+\begin{equation*}
+\sigma \simeq \left( -\frac{\partial^2\ln p}{\partial
+\lambda^2}\Big|_{\lambda_0} \right)^{-1/2},
+\end{equation*}
+where $\lambda_0$ is the most probable value of $\lambda$.
+\partmarks{5}
+\end{question}
+\end{document}