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Improving the Efficiency of Approximate Inference for Probabilistic Logical Models by means of Program Specialization
[article]
2011
arXiv
pre-print
We consider the task of performing probabilistic inference with probabilistic logical models. Many algorithms for approximate inference with such models are based on sampling. From a logic programming perspective, sampling boils down to repeatedly calling the same queries on a knowledge base composed of a static part and a dynamic part. The larger the static part, the more redundancy there is in these repeated calls. This is problematic since inefficient sampling yields poor approximations. We
arXiv:1112.5381v1
fatcat:ltnrjivn4fcijmnwd33obiobki