From 290f83716f3b3961afe752e4a5f0badb22024821 Mon Sep 17 00:00:00 2001 From: Stratis Ioannidis Date: Sat, 3 Nov 2012 14:33:33 -0700 Subject: beta fix --- intro.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 $\T{\beta}$. -- cgit v1.2.3-70-g09d2