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| author | Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)> | 2013-02-11 16:36:11 -0800 |
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| committer | Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)> | 2013-02-11 16:36:11 -0800 |
| commit | 574e1f7e48f238efada8bff73ba26fd6dd50aaac (patch) | |
| tree | 3dd0d7a62d97dbd82b079388a2a7fb63c03b3837 /problem.tex | |
| parent | 05da1a98508fdc6a7e2745d7dc649ccfb921edee (diff) | |
| download | recommendation-574e1f7e48f238efada8bff73ba26fd6dd50aaac.tar.gz | |
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Diffstat (limited to 'problem.tex')
| -rw-r--r-- | problem.tex | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/problem.tex b/problem.tex index d505280..3bdbb94 100644 --- a/problem.tex +++ b/problem.tex @@ -1,5 +1,5 @@ \label{sec:prel} -\subsection{Linear Regression and Experimental Design} +\subsection{Linear Regression and Experimental Design}\label{sec:edprelim} 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 |