  In this contribution we deal with the problem of learning an undirected graph
which encodes the conditional dependence relationship between variables of a
complex system, given a set of observations of this system. This is a very
central problem of modern data analysis and it comes out every time we want to
investigate a deeper relationship between random variables, which is different
from the classical dependence usually measured by the covariance.
  In particular, in this contribution we deal with the case of Gaussian
Graphical Models (GGMs) for which the system of variables has a multivariate
gaussian distribution. We study all the existing techniques for such a problem
and propose a smart implementation of the symmetric parallel regression
technique which turns out to be very competitive for learning sparse GGMs under
high dimensional data regime.
