From 05da1a98508fdc6a7e2745d7dc649ccfb921edee Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Mon, 11 Feb 2013 15:49:50 -0800
Subject: general

---
 problem.tex | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'problem.tex')

diff --git a/problem.tex b/problem.tex
index 536481a..d505280 100644
--- a/problem.tex
+++ b/problem.tex
@@ -77,8 +77,8 @@ prior covariance is the identity matrix, \emph{i.e.}, $R=I_d\in \reals^{d\times
 d}.$ Intuitively, this corresponds to the simplest prior, in which no direction
 of $\reals^d$ is a priori favored; equivalently, it also corresponds to the
 case where ridge regression estimation \eqref{ridge} performed by $\E$ has
-a penalty term $\norm{\beta}_2^2$. A generalization of our results to general
-matrices $R$ can be found in Section~\ref{sec:ext}.
+a penalty term $\norm{\beta}_2^2$. A generalization of our results to arbitrary 
+covariance matrices $R$ can be found in Section~\ref{sec:ext}.
 
  %Note that \eqref{dcrit} is a submodular set function, \emph{i.e.}, 
 %$V(S)+V(T)\geq V(S\cup T)+V(S\cap T)$ for all $S,T\subseteq \mathcal{N}$; it is also monotone, \emph{i.e.}, $V(S)\leq V(T)$ for all $S\subset T$.
-- 
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