Guido Previde Massara

Learn More
We introduce a methodology to construct sparse models from data by using information filtering networks. This method estimates the global sparse inverse covariance from a simple sum of local inverse covariances computed on small sub-parts of the network. Being based on local, low-dimensional, inversions this method is computationally very efficient and(More)
We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the Weighted Maximal Planar Graph problem. The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information retained by the network. TMFG uses as weights(More)
  • 1