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Equipe / Team : DUKe
Participants : Christine Sinoquet (correspondant),
Licence : Private
Mots clés : learning a forest of latent models, with choice of clustering method
Résumé :

The SYLVESTRA software program is dedicated to the modeling of dependencies within highly correlated (discrete) variables, in the high dimensional setting. The model inferred from the data is a forest of latent tree models (FLTM), that is a forest of tree-shaped Bayesian networks. SYLVESTRA implements an ascending clustering process to build an FLTM. Three clustering methods are currently available in this version.

Direction of the development of the versions of the SYLVESTRA software:
Christine Sinoquet (Associate Professor), project manager of the ANR SAMOGWAS project (2013-2017), LS2N / UMR CNRS 6004 (Digital Science Institute of Nantes), University of Nantes, France.



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