From 347489694571148e0cb940a96fe621a7394b5bfa Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Wed, 4 Feb 2015 15:49:24 -0500
Subject: rewriting of 3.1 + some minor changes

---
 paper/sections/model.tex | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'paper/sections/model.tex')

diff --git a/paper/sections/model.tex b/paper/sections/model.tex
index 25f55e0..d3403ef 100644
--- a/paper/sections/model.tex
+++ b/paper/sections/model.tex
@@ -120,6 +120,7 @@ Therefore, the independent cascade model is a Generalized Linear Cascade model w
 
 
 \subsection{Maximum Likelihood Estimation}
+\label{sec:mle}
 
 Recovering the model parameter $\Theta$ from observed influence cascades is the
 central question of the present work. Recovering the edges in $E$ from observed
@@ -149,7 +150,7 @@ of $m$ terms, each term $i\in\{1,\ldots,m\}$ only depending on $\theta_i$.
 Since this is equally true for $\|\Theta\|_1$, each column $\theta_i$ of
 $\Theta$ can be estimated by a separate optimization program:
 \begin{equation}\label{eq:pre-mle}
-    \hat{\theta}_i \in \argmax_{\theta} \frac{1}{n}\mathcal{L}_i(\theta_i\,|\,x^1,\ldots,x^n)
+    \hat{\theta}_i \in \argmax_{\theta} \mathcal{L}_i(\theta_i\,|\,x^1,\ldots,x^n)
     - \lambda\|\theta_i\|_1
 \end{equation}
 where we denote by ${\cal T}_i$ the time steps at which node $i$ is susceptible and:
-- 
cgit v1.2.3-70-g09d2

