  This research is about studying and comparing two different ways of building
complex networks. The main goal of our study is to find an effective way to
build networks, particularly when we have fewer observations than variables. We
construct networks estimating the partial correlation coefficient on Classic
Statistics (Inverse Method) and on Bayesian Statistics (Normal - Inverse
Wishart conjugate prior). In this current work, in order to solve the problem
of having less observations than variables, we propose a new methodology called
local partial correlation, which consists of selecting, for each pair of
variables, the other variables most correlated to the pair.We applied these
methods on simulated data and compared them through ROC curves. The most
attractive result is that, even though it has high computational costs, to use
Bayesian inference on trees is better when we have less observations than
variables. In other cases, both approaches present satisfactory results.
