From 15e4871fc224e9e74c93b772b15aea7031f262ab Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Thu, 5 Nov 2015 09:35:44 -0500
Subject: adding simple bayes function

---
 notes/extensions.tex | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'notes/extensions.tex')

diff --git a/notes/extensions.tex b/notes/extensions.tex
index dd9933a..cc247ef 100644
--- a/notes/extensions.tex
+++ b/notes/extensions.tex
@@ -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}
-- 
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