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authorThibaut Horel <thibaut.horel@gmail.com>2013-07-08 22:25:21 +0200
committerThibaut Horel <thibaut.horel@gmail.com>2013-07-08 22:25:21 +0200
commit825d56f2f4e53eb162270fe4b3fa002f8b87a9fc (patch)
treed37c0cf68aa5ab0fba1b6f37f9707a5e475f6a7e
parent8360348b640a56c004730036025b0f3f9f9ed9a2 (diff)
downloadrecommendation-825d56f2f4e53eb162270fe4b3fa002f8b87a9fc.tar.gz
Fix micro bug
-rw-r--r--problem.tex5
1 files changed, 4 insertions, 1 deletions
diff --git a/problem.tex b/problem.tex
index 15f0c11..c21135b 100644
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The theory of experimental design \cite{pukelsheim2006optimal,atkinson2007optimum,chaloner1995bayesian} considers the following formal setting. % studies how an experimenter \E\ should select the parameters of a set of experiments she is about to conduct. In general, the optimality of a particular design depends on the purpose of the experiment, \emph{i.e.}, the quantity \E\ is trying to learn or the hypothesis she is trying to validate. Due to their ubiquity in statistical analysis, a large literature on the subject focuses on learning \emph{linear models}, where \E\ wishes to fit a linear function to the data she has collected.
%Putting cost considerations aside
-Suppose that an experimenter \E\ wishes to conduct $k$ among $n$ possible experiments. Each experiment $i\in\mathcal{N}\defeq \{1,\ldots,n\}$ is associated with a set of parameters (or features) $x_i\in \reals^d$, normalized so that $$b\leq \|x_i\|_2\leq 1,$$ for some $b>0$. Denote by $S\subseteq \mathcal{N}$, where $|S|=k$, the set of experiments selected; upon its execution, experiment $i\in S$ reveals an output variable (the ``measurement'') $y_i$, related to the experiment features $x_i$ through a linear function, \emph{i.e.},
+Suppose that an experimenter \E\ wishes to conduct $k$ among $n$ possible
+experiments. Each experiment $i\in\mathcal{N}\defeq \{1,\ldots,n\}$ is
+associated with a set of parameters (or features) $x_i\in \reals^d$, normalized
+so that $$b\leq \|x_i\|^2_2\leq 1,$$ for some $b>0$. Denote by $S\subseteq \mathcal{N}$, where $|S|=k$, the set of experiments selected; upon its execution, experiment $i\in S$ reveals an output variable (the ``measurement'') $y_i$, related to the experiment features $x_i$ through a linear function, \emph{i.e.},
\begin{align}\label{model}
\forall i\in\mathcal{N},\quad y_i = \T{\beta} x_i + \varepsilon_i
\end{align}