From 0a2d7e43c9df6cdfb27b7a42177e9ff1a4e087ef Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Mon, 2 Feb 2015 22:57:08 -0500
Subject: small changes

---
 paper/sections/model.tex | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

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

diff --git a/paper/sections/model.tex b/paper/sections/model.tex
index 44236da..5934590 100644
--- a/paper/sections/model.tex
+++ b/paper/sections/model.tex
@@ -1,7 +1,7 @@
 We consider a graph ${\cal G}= (V, E, \Theta)$, where $\Theta$ is a $|V|\times |V|$ matrix of parameters describing the edge weights of $\mathcal{G}$. Let $m\defeq |V|$. For each node $j$, let $\Theta_{j}$ be the $j^{th}$ column vector of $\Theta$. Intuitively, $\Theta_{i,j}$ captures the ``influence'' of node $i$ on node $j$. A \emph{Cascade model} is a Markov process over a finite state space $\{0, 1, \dots, K-1\}^V$ with the following properties:
 \begin{enumerate}
 \item Conditioned on the previous time step, the transition probabilities for each node $i \in V$ are mutually independent.
-\item Of the $K$ possible states, there exists a \emph{contagious state} such that all transition probabilities of the Markov process can be expressed as a function of the graph parameters $\Theta$ and the set of ``contagious nodes'' at the previous time step.
+\item Of the $K$ possible states, there exists a \emph{contagious state} such that all transition probabilities of the Markov process are either constant or can be expressed as a function of the graph parameters $\Theta$ and the set of ``contagious nodes'' at the previous time step.
 \item The initial probability over $\{0, 1, \dots, K-1\}^V$ of the Cascade Model is such that all nodes can eventually reach a \emph{contagious state} with non-zero probability. The ``contagious'' nodes at $t=0$ are called \emph{source nodes}.
 \end{enumerate}
 
@@ -105,12 +105,11 @@ Despite their obvious differences, both models share a common similarity: condit
 
 \begin{definition}
 \label{def:glcm}
-Let us denote by $\{\mathcal{F}_t, t\in\ints\}$ the natural filtration induced by the state of ${\cal G}$ up to time step t. A \emph{generalized linear cascade} is a cascade model characterized by the following equation:
+A \emph{generalized linear cascade} is a cascade model characterized by the following equation:
 \begin{displaymath}
-    \P[X^{t+1}=x\,|\, \mathcal{F}_t] =
-    \prod_{i=1}^m f_{X^t_i,x_i}(\Theta_i \cdot X^t)
+\forall j \in V, \; \P[X^{t+1}_j=x\,|\, X^t] = f(\Theta_j \cdot X^t)
 \end{displaymath}
-where $f_{s,p}:\mathbb{R}\to[0,1]$ for each state pair $(s,p) \in \{0, \dots, K-1 \}$
+where $f:\mathbb{R}\to[0,1]$
 \end{definition}
 
 
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