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Journal Articles Interdisciplinary Information Sciences Year : 2013

Pairwise MRF Models Selection for Traffic Inference

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Cyril Furtlehner
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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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Dates and versions

hal-00865089 , version 1 (24-09-2013)

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Cyril Furtlehner. Pairwise MRF Models Selection for Traffic Inference. 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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