A hybrid methodology for learning belief networks: BENEDICT. Previous algorithms for the construction of belief networks structures from data are mainly based either on independence criteria or on scoring metrics. The aim of this paper is to present a hybrid methodology that is a combination of these two approaches, which benefits from characteristics of each one, and to develop two operative algorithms based on this methodology. Results of the evaluation of the algorithms on the well-known Alarm network are presented, as well as the algorithms performance issues and some open problems.

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  4. Gheisari, S.; Meybodi, M. R.: BNC-PSO: structure learning of Bayesian networks by particle swarm optimization (2016)
  5. Szántai, Tamás; Kovács, Edith: Hypergraphs as a mean of discovering the dependence structure of a discrete multivariate probability distribution (2012)
  6. Gámez, José A.; Mateo, Juan L.; Puerta, José M.: Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood (2011)
  7. Zgurovskii, M. Z.; Bidyuk, P. I.; Terent’ev, A. N.: Methods of constructing Bayesian networks based on scoring functions (2008)
  8. Gámez, José A.; Mateo, Juan L.; Puerta, José M.: A fast hill-climbing algorithm for Bayesian networks structure learning (2007)
  9. Acid, Silvia; Campos, Luis M.; Castellano, Javier G.: Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs (2005)
  10. Cheng, Jie; Greiner, Russell; Kelly, Jonathan; Bell, David; Liu, Weiru: Learning Bayesian networks from data: An information-theory based approach (2002)
  11. de Campos, Luis M.; Fernández-Luna, Juan M.; Gámez, José A.; Puerta, José M.: Ant colony optimization for learning Bayesian networks. (2002)
  12. Acid, Silvia; de Campos, Luis M.: A hybrid methodology for learning belief networks: BENEDICT (2001)
  13. de Campos, Luis M.; Huete, Juan F.: A new approach for learning belief networks using independence criteria (2000)