From 0f3c28b41f47326fac78897d770eabf64ceb6c98 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Sun, 23 Nov 2014 11:19:17 -0500
Subject: Fix typos

---
 notes/formalisation.tex | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

(limited to 'notes/formalisation.tex')

diff --git a/notes/formalisation.tex b/notes/formalisation.tex
index 871fe50..bdd6aab 100644
--- a/notes/formalisation.tex
+++ b/notes/formalisation.tex
@@ -56,8 +56,8 @@ Two main goals:
         chosen uniformly at random) and on the cascade diffusion process. Such
         a model is probably not very realistic, but is necessary to obtain
         theoretical guarantees. The unrealism of the model is not a problem
-        \emph{per se}; the eventual validity of this approach will be
-        experimental anyway.
+        \emph{per se}; the eventual validity of this approach will be confirmed
+        experimentally anyway.
     \item It seems likely that we will want the rows of the sensing matrix to
         be independent realizations of the same distribution. However,
         \textbf{this will never be the case} if we have one row for each time
@@ -67,17 +67,17 @@ Two main goals:
         cascade.
     \item Something which makes this problem very different from the usual
         compressed sensing problems is that the measurements depend on the
-        signal we wish to recover: cascades propagates along the edges
-        contained in the signal. More precisely, not only the rows of the
-        sensing matrix are correlated (if we do not aggregate them by cascade)
-        but they are correlated through the signal. Is this a problem? I don't
-        know, this looks scary though.
+        signal we wish to recover: cascades propagate along the edges contained
+        in the signal. More precisely, not only the rows of the sensing matrix
+        are correlated (if we do not aggregate them by cascade) but also they
+        are correlated through the signal. Is this a problem? I don't know,
+        this looks scary though.
     \item A slightly more general model which we will need in the independent
         cascade case considers a signal $s\in[0,1]^{V\times V}$. We can always
         go from this model to the binary case by first recovering the
         non-binary $s$ and then applying a thresholding procedure. But if we
-        only care about recovering the support of $s$, can we do better? (TO
-        DO: read stuff about support recovery).
+        only care about recovering the support of $s$, can we do better than
+        this two-step process? (TO DO: read stuff about support recovery).
 \end{itemize}
 \end{remark}
 
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