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diff --git a/finale/sections/appendix.tex b/finale/sections/appendix.tex index e69de29..b53de70 100644 --- a/finale/sections/appendix.tex +++ b/finale/sections/appendix.tex @@ -0,0 +1,22 @@ +\begin{figure*} +\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*} |