A General Framework for Anytime Approximation in Probabilistic Databases

06/26/2018

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by   Maarten Van den Heuvel, et al.

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Anytime approximation algorithms for computing query probabilities over probabilistic databases can be of great use to statistical learning tasks. They have so far been based on either sampling or a branch-and-bound approach using model-based bounds. We present here a general framework for the branch-and-bound approach, where we extend the possible bounding methods by using dissociation, which yields tighter bounds.