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Learning Clique Forests
Through this move the decomposability and calculation of scores is performed incrementally at the variable (rather than edge) level, and this provides better computational performance and an intuitive application of multivariate statistical tests.
Parsimonious modeling with Information Filtering Networks
We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors.
Network Filtering for Big Data: Triangulated Maximally Filtered Graph
TMFG uses as weights any arbitrary similarity measure to arrange data into a meaningful network structure that can be used for clustering, community detection and modeling.
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