From 240bbb99052ea5e873128649d01228442d889a33 Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Sun, 7 Jul 2013 14:18:31 -0700
Subject: related

---
 problem.tex | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'problem.tex')

diff --git a/problem.tex b/problem.tex
index 89ffa9d..1001d7b 100644
--- a/problem.tex
+++ b/problem.tex
@@ -54,8 +54,7 @@ Maximizing $I(\beta;y_S)$ is therefore equivalent to maximizing $\log\det(R+ \T{
 $D$-optimality criterion
 \cite{pukelsheim2006optimal,atkinson2007optimum,chaloner1995bayesian}. 
 
-Note that the estimator $\hat{\beta}$ is a linear map of $y_S$. As $y_S$ is a multidimensional normal r.v., so is $\hat{\beta}$; in particular, $\hat{\beta}$ has 
-covariance $\sigma^2(R+\T{X_S}X_S)^{-1}$. As such, maximizing $I(\beta;y_S)$ can alternatively be seen as a means of reducing the uncertainty on estimator $\hat{\beta}$ by minimizing the product of the eigenvalues of its covariance (as the latter equals the determinant).
+%Note that the estimator $\hat{\beta}$ is a linear map of $y_S$. As $y_S$ is a multidimensional normal r.v., so is $\hat{\beta}$; in particular, $\hat{\beta}$ has covariance $\sigma^2(R+\T{X_S}X_S)^{-1}$. As such, maximizing $I(\beta;y_S)$ can alternatively be seen as a means of reducing the uncertainty on estimator $\hat{\beta}$ by minimizing the product of the eigenvalues of its covariance (as the latter equals the determinant).
 
 %An alternative interpretation, given that $(R+ \T{X_S}X_S)^{-1}$ is the covariance of the estimator $\hat{\beta}$, is that it tries to minimize the 
 %which is indeed a function of the covariance matrix $(R+\T{X_S}X_S)^{-1}$.
-- 
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