  In this paper, we present a simple non-parametric method for learning the
structure of undirected graphs from data that drawn from an underlying unknown
distribution. We propose to use Brownian distance covariance to estimate the
conditional independences between the random variables and encodes pairwise
Markov graph. This framework can be applied in high-dimensional setting, where
the number of parameters much be larger than the sample size.
