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Relational Probabilistic Graphical Models

Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

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.

Keywords

Bayesian Network Propositional Logic Predicate Logic Logical Formula Inductive Logic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

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

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