MEBN: a language for first-order Bayesian knowledge bases
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Publication:2389617
DOI10.1016/j.artint.2007.09.006zbMath1182.68288OpenAlexW2053181911MaRDI QIDQ2389617
Publication date: 17 July 2009
Published in: Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.artint.2007.09.006
knowledge representationBayesian networkprobabilistic logicmulti-entity Bayesian networkgraphical probability modelsuncertainty in artificial intelligence
Knowledge representation (68T30) Reasoning under uncertainty in the context of artificial intelligence (68T37)
Related Items (9)
Learning first-order probabilistic models with combining rules ⋮ PR-OWL - a language for defining probabilistic ontologies ⋮ A new probabilistic constraint logic programming language based on a generalised distribution semantics ⋮ Structured probabilistic inference ⋮ The complexity of Bayesian networks specified by propositional and relational languages ⋮ PRM inference using Jaffray \& Faÿ's local conditioning ⋮ Automated compilation of object-oriented probabilistic relational models ⋮ A Survey of Directed Entity-Relation--Based First-Order Probabilistic Languages ⋮ MEBN
Uses Software
Cites Work
- Answering queries from context-sensitive probabilistic knowledge bases
- An analysis of first-order logics of probability
- Probabilistic Horn abduction and Bayesian networks
- Complex probabilistic modeling with recursive relational Bayesian networks
- Parameter learning in object-oriented Bayesian networks
- From statistical knowledge bases to degrees of belief
- Probabilistic Networks and Expert Systems
- 10.1162/jmlr.2003.3.4-5.679
- 10.1162/153244304773633852
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