- Authors:
- Kristian Kersting,
- Luc De Raedt
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. They are a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developed in both areas carry over.
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- Wuillemin P and Torti L (2018). Structured probabilistic inference, International Journal of Approximate Reasoning, 53:7, (946-968), Online publication date: 1-Oct-2012.
- Wang J, Byrnes J, Valtorta M and Huhns M (2012). On the combination of logical and probabilistic models for information analysis, Applied Intelligence, 36:2, (472-497), Online publication date: 1-Mar-2012.
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- Wan H Belief Logic Programming with Cyclic Dependencies Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems, (150-165)
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- Tresp V, Bundschus M, Rettinger A and Huang Y Towards Machine Learning on the Semantic Web Uncertainty Reasoning for the Semantic Web I, (282-314)
- Riguzzi F (2008). ALLPAD, Machine Language, 70:2-3, (207-223), Online publication date: 1-Mar-2008.
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- Predoiu L Probabilistic information integration and retrieval in the semantic web Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference, (936-940)
- Getoor L and Grant J (2006). PRL, Machine Language, 62:1-2, (7-31), Online publication date: 1-Feb-2006.
- Srinivasan A, Page D, Camacho R and King R (2006). Quantitative pharmacophore models with inductive logic programming, Machine Language, 64:1-3, (65-90), Online publication date: 1-Sep-2006.
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