An exact approach to learning probabilistic relational model

Abstract : Probabilistic Graphical Models (PGMs) offer a popular framework including a variety of statistical formalisms, such as Bayesian networks (BNs). These latter are able to depict real-world situations with high degree of uncertainty. Due to their power and flexibility, several extensions were proposed, ensuring thereby the suitability of their use. Probabilistic Relational Models (PRMs) extend BNs to work with relational databases rather than propositional data. Their construction represents an active area since it remains the most complicated issue. Only few works have been proposed in this direction, and most of them don’t guarantee an optimal identification of their dependency structure. In this paper we intend to propose an approach that ensures returning an optimal PRM structure. It is inspired from a BN method whose performance was already proven.
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Communication dans un congrès
8th International Conference on Probabilistic Graphical Models (PGM 2016), 2016, Lugano, Switzerland. pp.171-182, Proceedings of the 8th International Conference on Probabilistic Graphical Models (PGM 2016)
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Nourhene Ettouzi, Philippe Leray, Montassar Ben Messaoud. An exact approach to learning probabilistic relational model. 8th International Conference on Probabilistic Graphical Models (PGM 2016), 2016, Lugano, Switzerland. pp.171-182, Proceedings of the 8th International Conference on Probabilistic Graphical Models (PGM 2016). 〈hal-01347804〉

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