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-rw-r--r--notes/extensions.tex6
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diff --git a/notes/extensions.tex b/notes/extensions.tex
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@@ -31,7 +31,11 @@ network learning however, we can place more informative priors. We can
\item Take into account common graph structures, such as triangles
\end{itemize}
-We can sample from the posterior by MCMC.
+We can sample from the posterior by MCMC. This might not be a very fast solution
+however. In the case of the independent cascade model, there is an easier
+solution: we can use the EM algorithm to compute the posterior, by using the
+parent that \emph{does} infect us (if at all) as the latent variable in the
+model.
\subsection{Active Learning}