First-Order Bayes-Ball for CP-Logic
作者： Jan Struyf , Wannes Meert , Hendrik Blockeel , Nima Taghipour
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摘要: Ecient probabilistic inference is key to the success of statistical relational learning. One issue that aects cost presence irrelevant random variables. The Bayes-ball algorithm can identify such variables in a propositional Bayesian network. This paper presents lifted version Bayes-ball, which works directly on rst-order level, and shows how this applies CP-logic inference.
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