\subsection{Bayesian Experimental Design}
In this section, we extend our results to Bayesian experimental design \cite{chaloner1995bayesian}. In particular, we show that our choice of objective function \eqref{...} has a natural interpration in this context, further motivating its selection, and Theorem~\ref{...} has a natural generalization to this context.

In the Bayesian setting, it is assumed that the experimenter has a prior distribution on $\beta$: in particular, $\beta$ is assumed to be sampled from a multivariate normal distribution with zero mean  and covariance $\sigma^2R\in \reals^{d^2}$ (where $\sigma^2$ is the noise variance). 
The experimenter estimates $\beta$ through \emph{maximum a posteriori estimation}: \emph{i.e.}, finding the parameter which maximizes the posterior distribution of $\beta$ given the observations $y_S$. Under the linearity assumption \eqref{model} and the gaussian prior on $\beta$, maximum a posteriori estimation leads to the following maximization \cite{hastie}: FIX!
\begin{displaymath}
    \hat{\beta} = \argmin_{\beta\in\reals^d} \sum_i (y_i - \T{\beta}x_i)^2
    + \sum_i \norm{R\beta}_2^2
\end{displaymath}
This optimization, commonly known as \emph{ridge regression}, includes an additional penalty term compared to the least squares estimation \eqref{leastsquares}.
Let $\entropy(\beta)$ be the entropy of $\beta$ under this distribution, and $\entropy(\beta\mid y_S)$ the  entropy of $\beta$ conditioned on the experiment outcomes $Y_S$, for some $S\subseteq \mathcal{N}$. In this setting, a natural objective to select a set of experiments $S$ that maximizes her \emph{information gain}:
$$ I(\beta;y_S) = \entropy(\beta)-\entropy(\beta\mid y_S). $$

Assuming normal noise variables, the information gain is  equal (upto a constant) to the following value function \cite{chaloner1995bayesian}:
\begin{align}
V(S)  = \frac{1}{2}\log\det(R + \T{X_S}X_S)\label{bayesianobjective}
\end{align}
Our objective \eqref{,,,} clearly follows from \eqref{bayesianobjective} by setting $R=I_d$. Hence, our optimization can be interpreted as a maximization of the information gain when the prior distribution has a covariance $\sigma^2 I_d$, and the experimenter is solving a ridge regression problem with penalty term $\norm{x}_2^2$. 

Moreover, our results can be extended to the general Bayesian case, by replacing $I_d$ with the positive semidefinite matrix $R$:

TODO: state theorem, discuss dependence on $\det R$.

\subsection{Beyond Linear Models}
TODO: Independent noise model. Captures models such as logistic regression, classification, etc. Arbitrary prior. Show that change in the entropy is submodular (cite Krause, Guestrin). 
