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| -rw-r--r-- | intro.tex | 2 |
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@@ -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 $\T{\beta}$. |