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See figure next page.
\begin{figure*}[h!]
\centering
\subfigure[][50 cascades]{
\includegraphics[scale=.4]{../simulation/plots/2015-11-05_22:52:30.pdf}}\hspace{1em}%
\subfigure[][100 cascades]{
\includegraphics[scale=.4]{../simulation/plots/2015-11-05_22:52:47.pdf}}\\
\subfigure[][150 cascades]{
\includegraphics[scale=.4]{../simulation/plots/2015-11-05_22:53:24.pdf}}\hspace{1em}%
\subfigure[][200 cascades]{
\includegraphics[scale=.4]{../simulation/plots/2015-11-05_22:55:39.pdf}}\\
\subfigure[][250 cascades]{
\includegraphics[scale=.4]{../simulation/plots/2015-11-05_22:57:26.pdf}}\hspace{1em}%
\subfigure[][1000 cascades]{
\includegraphics[scale=.4]{../simulation/plots/2015-11-05_22:58:29.pdf}}
\caption{Bayesian Inference of $\Theta$ with MCMC using a $Beta(1, 1)$ prior on
each edge. For each figure, the plot $(i, j)$ on the $i^{th}$ row and $j^{th}$
column represent a histogram of samples taken from the posterior of the
corresponding edge $\Theta_{i, j}$. The red line indicates the true value of the
edge weight. If an edge does not exist (has weight $0$) the red line is
confounded with the y axis.}
\label{betapriorbayeslearning}
\end{figure*}
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