From 05da1a98508fdc6a7e2745d7dc649ccfb921edee Mon Sep 17 00:00:00 2001 From: Stratis Ioannidis Date: Mon, 11 Feb 2013 15:49:50 -0800 Subject: general --- problem.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'problem.tex') diff --git a/problem.tex b/problem.tex index 536481a..d505280 100644 --- a/problem.tex +++ b/problem.tex @@ -77,8 +77,8 @@ prior covariance is the identity matrix, \emph{i.e.}, $R=I_d\in \reals^{d\times d}.$ Intuitively, this corresponds to the simplest prior, in which no direction of $\reals^d$ is a priori favored; equivalently, it also corresponds to the case where ridge regression estimation \eqref{ridge} performed by $\E$ has -a penalty term $\norm{\beta}_2^2$. A generalization of our results to general -matrices $R$ can be found in Section~\ref{sec:ext}. +a penalty term $\norm{\beta}_2^2$. A generalization of our results to arbitrary +covariance matrices $R$ can be found in Section~\ref{sec:ext}. %Note that \eqref{dcrit} is a submodular set function, \emph{i.e.}, %$V(S)+V(T)\geq V(S\cup T)+V(S\cap T)$ for all $S,T\subseteq \mathcal{N}$; it is also monotone, \emph{i.e.}, $V(S)\leq V(T)$ for all $S\subset T$. -- cgit v1.2.3-70-g09d2