  A fundamental method of reconstructing networks, e.g. in the context of gene
regulation, relies on the precision matrix (the inverse of the
variance-covariance matrix) as an indicator which variables are associated with
each other. The precision matrix assumes Gaussian data and its entries are zero
for those pairs of variable which are conditionally independent. Here, we
propose the Distance Precision Matrix which is based on a measure of possibly
non-linear association, the distance covarince. We provide evidence that the
Distance Precision Matrix can successfully compute networks from non-linear
data and does so in a very consistent manner across many data situations.
