From 290f83716f3b3961afe752e4a5f0badb22024821 Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Sat, 3 Nov 2012 14:33:33 -0700
Subject: beta fix

---
 intro.tex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'intro.tex')

diff --git a/intro.tex b/intro.tex
index 9b1be51..9f98137 100644
--- a/intro.tex
+++ b/intro.tex
@@ -2,7 +2,7 @@ There is a mature area of experimental design, where the setting is as follows.
 There is an {\em experimenter}  \E\ with access to a population of $n$ members. 
 Each member $i\in  \{1,\ldots,n\}$ is associated with a set of parameters (or features) $x_i\in \reals^d$, 
 known to the experimenter. 
-\E\ wishes to perform an experiment:  the outcome for a member $i$ is denoted $y_i$, which is unknown to \E\ before the experiment is performed.  Typically, \E\ has a hypothesis of the relationship between $x_i$'s and $y_i$'s, such as, say linear, i.e.,  $y_i \approx  \T{\beta} x_i$., and the experiment lets \E\ derive some estimate of \T{\beta}$. 
+\E\ wishes to perform an experiment:  the outcome for a member $i$ is denoted $y_i$, which is unknown to \E\ before the experiment is performed.  Typically, \E\ has a hypothesis of the relationship between $x_i$'s and $y_i$'s, such as, say linear, i.e.,  $y_i \approx  \T{\beta} x_i$., and the experiment lets \E\ derive some estimate of $\T{\beta}$. 
  
 
 
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