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Pairwise MRF Models Selection for Traffic Inference

Cyril Furtlehner 1
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : We survey some recent work where, motivated by traffic inference, we design in parallel two concurrent models, an Ising and a Gaussian ones, with the constraint that they are suitable for ''belief-propagation'' based inference. In order to build these model, we study how a Bethe mean-field solution to inverse problems obtained with a maximum spanning tree of pairwise mutual information, can serve as a reference point for further perturbation procedures. We consider three different ways along this idea: the first one is based on an explicit natural gradient formula; the second one is a link by link construction based on iterative proportional scaling; the last one relies on a duality transformation leading to a loop correction propagation algorithm on a dual factor graph.
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Submitted on : Tuesday, September 24, 2013 - 10:48:20 AM
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Cyril Furtlehner. Pairwise MRF Models Selection for Traffic Inference. Interdisciplinary Information Sciences, Editorial Committee of the Interdisciplinary Information Sciences, 2013, Special Issue: The 4th Young Scientist Meeting on Statistical Physics and Information Processing in Sendai, 19 (1), pp.17-22. ⟨10.4036/iis.2013.17⟩. ⟨hal-00865089⟩



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