The aim of this workshop is to bridge the gap between Statistics and Artificial Intelligence communities where approximate inference methods for GM are developped. We are primarily interested in algorithmic aspects of probabilistic (e.g. Markov random fields, Bayesian networks, influence diagrams), deterministic (e.g. Constraint Satisfaction Problems, SAT, weighted variants, Generalized Additive Independence models) or hybrid (e.g. Markov logic networks) models.
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