Relational Probabilistic Graphical Models

  • Luis Enrique SucarEmail author
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


This chapter introduces relational probabilistic graphical models (RPGMs), which combine the expressive power of predicate logic with the uncertain reasoning capabilities of probabilistic graphical models. First, a brief review of propositional and predicate logic is presented. Then, two different relational probabilistic formalisms are described: probabilistic relational models and Markov logic networks. Finally, the application of the two previous approaches is illustrated in two domains, student modeling for a virtual laboratory and visual object recognition based on symbol-relational grammars.


Bayesian Network Propositional Logic Predicate Logic Logical Formula Inductive Logic Programming 
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  1. 1.
    Domingos, P., Richardson, M.: Markov Logic: A Unifying Framework for Statistical Relational Learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning, pp. 339–371. MIT Press, Cambridge (2007)Google Scholar
  2. 2.
    Ferrucci, F., Pacini, G., Satta, G., Sessa, M.I., Tortora, G., Tucci, M., Vitiello, G.: Symbol-Relation Grammars: A Formalism for Graphical Languages. Information and Computation 131(1), 1–46 (1996)zbMATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Friedman, N., Getoor, L., Koller, D., Pfeffe, A.: Learning Probabilistic Relational Models. In: Proceeding of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1300–1309 (1999)Google Scholar
  4. 4.
    Genesereth, M.R., Nilsson, N.J.: Logical Foundations of Artificial Intelligence. Morgan Kaufmann (1988)Google Scholar
  5. 5.
    Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)zbMATHGoogle Scholar
  6. 6.
    Koller D.: Probabilistic Relational Models. In: Proceedings of the 9th International Workshop on Inductive Logic Programming. Lecture Notes in Artificial Intelligence, vol. 1634, Springer, 3–13 (1999)Google Scholar
  7. 7.
    Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York (1994)zbMATHGoogle Scholar
  8. 8.
    Lemmon, E.J.: Beginning Logic. Hackett Publishing Company (1978)Google Scholar
  9. 9.
    Newton-Smith, W.H.: Logic: An Introductory Course. Routledge, Milton Park (1985)zbMATHCrossRefGoogle Scholar
  10. 10.
    Nienhuys-Cheng, S., de Wolf, R.: Foundations of Inductive Logic Programming. Springer-Verlag, Berlin (1991)Google Scholar
  11. 11.
    Richardson, M., Domingos, P.: Markov logic networks. Machine Learning. 62(1–2), 107–136 (2006)CrossRefGoogle Scholar
  12. 12.
    Ruiz, E., Sucar, L.E.: An object recognition model based on visual grammars and Bayesian networks. In: Proceedings of the Pacific Rim Symposium on Image and Video Technology, LNCS 8333, pp. 349–359, Springer-Verlag (2014)Google Scholar
  13. 13.
    Sucar, L.E., Noguez, J.: Student Modeling. In: O. Pourret, P. Naim, B. Marcot (eds.) Bayesian Belief Networks: A Practical Guide to Applications, pp. 173–186. Wiley and Sons (2008)Google Scholar

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© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)Santa María TonantzintlaMexico

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