Guido Previde Massara

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We recruited 50 cancer patients and their caregivers with the aim of extending our knowledge of emotional, personality and psychosocial variables, and comparing their reciprocal experience of the disease. The patients and caregivers were administered four of the questionnaires included in the Cognitive Behavioral Assessment 2.0, the Family Strain(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)
We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is(More)
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