Learning Bayesian Networks
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Learning directed probabilistic logical models: ordering-search versus structure-search
Annals of Mathematics and Artificial Intelligence
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Cutset sampling for Bayesian networks
Journal of Artificial Intelligence Research
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic inductive logic programming: theory and applications
Probabilistic inductive logic programming: theory and applications
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as Bayesian networks, this is not the case for relational probabilistic models. In this paper we show that directed relational probabilistic models often exhibit CSI by identifying three different sources of CSI in such models (two of which are inherent to the relational character of the models). It is known that CSI can be used to speed up probabilistic inference. In this paper we show how to do this in a general way for approximate inference based on Gibbs sampling. We perform experiments on real-world data to analyze the influence of the three different types of CSI. The results show that exploiting CSI yields speedups of up to an order of magnitude.