1 files changed, 22 insertions, 19 deletions
diff --git a/poster/Finale_poster/poster.tex b/poster/Finale_poster/poster.tex
index 618d501..aef5490 100644
--- a/poster/Finale_poster/poster.tex
+++ b/poster/Finale_poster/poster.tex
@@ -1,6 +1,6 @@
 \documentclass[final]{beamer}
 \usepackage[utf8]{inputenc}
-\usepackage[scale=1.6]{beamerposter} % Use the beamerposter package for laying
+\usepackage[scale=1.8]{beamerposter} % Use the beamerposter package for laying
 \usetheme{confposter} % Use the confposter theme supplied with this template
 
 \usepackage{framed, amsmath, amsthm, amssymb}
@@ -30,11 +30,11 @@
 %	TITLE SECTION
 %----------------------------------------------------------------------------------------
 
-\title{Inferring Graphs from Cascades} % Poster title
+\title{Bayesian and Active Learning for Graph Inference} % Poster title
 
 \author{Thibaut Horel, Jean Pouget-Abadie} % Author(s)
 
-\institute{Harvard University} % Institution(s)
+%\institute{Harvard University} % Institution(s)
 %----------------------------------------------------------------------------------------
 \begin{document}
 \addtobeamertemplate{block end}{}{\vspace*{2ex}} % White space under blocks
@@ -53,33 +53,36 @@
 %----------------------------------------------------------------------------------------
 
 
-\vspace{- 12.2 cm}
-\begin{center}
-{\includegraphics[scale=2.5]{../images/SEASLogo_RGB.png}}
-\end{center}
-
-\vspace{5 cm}
-
 \begin{block}{Problem}
   \emph{How to recover an unknown network from the observation of contagion
   cascades?}
-  \vspace{.5cm}
+  \vspace{1em}
   \begin{itemize}
-  \item {\bf Observe} $X^t_c$ (infections at time $t$ in cascade $c$)
-  \item {\bf Objective}: find $\{\theta_{ij}\}$ (graph weight matrix)
+      \item \textbf{Observe:} state (infected or not) of nodes over time.
+      \item \textbf{Objective:} learn $\Theta$, matrix of edge weights.
 \end{itemize}
 \end{block}
 \vspace{1cm}
 
 \begin{block}{\bf Contagion Model~\cite{}}
 \begin{itemize}
-  \item Discrete time
-  \item Infections drawn indep.~for each node conditioned on previous step
-  \item Generalized Linear Model parametrization:
-    \begin{framed}
-    $$\mathbb{P}(X^{t+1}_j = 1 | X^t) = f(\Theta_j \cdot X^t)$$
-    \end{framed}
+    \item $X^t\in\{0,1\}^N$: state of the network at time $t$
+    \item At $t=0$, $X^0$ drawn from \emph{source distribution}
+    \item For $t=1,2,\dots$:
+        \begin{itemize}
+            \item $X^t$ only depends on $X^{t-1}$
+            \item for each node $j$, new state drawn independently with:
+                \begin{displaymath}
+                    \mathbb{P}(X^{t+1}_j = 1 | X^t) = f(\Theta_j \cdot X^t)
+                \end{displaymath}
+            ($f$: link function of the cascade model)
+\end{itemize}
 \end{itemize}
+  \vspace{1em}
+\begin{figure}
+  \centering
+  \includegraphics[scale=2]{drawing.pdf}
+\end{figure}
 \end{block}
 
 \begin{block}{MLE}