From 3173920dc2e45ba7998d5cf0745e7de1a14eb68d Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Sun, 1 Feb 2015 21:13:06 -0500
Subject: small touch ups to results

---
 paper/sections/model.tex | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

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

diff --git a/paper/sections/model.tex b/paper/sections/model.tex
index 3bc8b20..e35aa4a 100644
--- a/paper/sections/model.tex
+++ b/paper/sections/model.tex
@@ -45,9 +45,7 @@ cascade model.
 \subsection{Examples}
 \label{subsec:examples}
 
-In this section, we show that both the Independent Cascade Model and the Voter
-model are Generalized Linear Cascades. The Linear Threshold model will be
-discussed in Section~\ref{sec:linear_threshold}.
+In this section, we show that the well-known Independent Cascade Model and the Voter model are Generalized Linear Cascades. The Linear Threshold model will be discussed in Section~\ref{sec:linear_threshold}.
 
 \subsubsection{Independent Cascade Model}
 
@@ -75,7 +73,7 @@ Defining $\Theta_{i,j} \defeq \log(1-p_{i,j})$, this can be rewritten as:
     = 1 - e^{\inprod{\theta_j}{X^t}}
 \end{equation}
 which is a Generalized Linear Cascade model with inverse link function $f(z)
-= z$.
+= 1 - e^z$.
 
 \subsubsection{The Voter Model}
 
@@ -90,7 +88,7 @@ step $t$, then we have:
 \mathbb{P}\left[X^{t+1}_j = 1 | X^t \right] = \sum_{i=1}^m \Theta_{i,j} X_i^t = \inprod{\theta_j}{X^t}
 \tag{V}
 \end{equation}
-which is again a Generalized Linear Cascade model with inverse link function
+which is a Generalized Linear Cascade model with inverse link function
 $f(z) = z$.
 
 \subsection{Maximum Likelihood Estimation}
-- 
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