Improving the Efficiency of Approximate Inference for Probabilistic Logical Models by means of Program Specialization [article]

Daan Fierens
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
more » ... how how to apply logic program specialization to make sampling-based inference more efficient. We develop an algorithm that specializes the definitions of the query predicates with respect to the static part of the knowledge base. In experiments on real-world data we obtain speedups of up to an order of magnitude, and these speedups grow with the data-size.
arXiv:1112.5381v1 fatcat:ltnrjivn4fcijmnwd33obiobki