A General Framework for Anytime Approximation in Probabilistic Databases
Databases
2018-07-04 v2
Abstract
Anytime approximation algorithms that compute the probabilities of queries over probabilistic databases can be of great use to statistical learning tasks. Those approaches have been based so far on either (i) sampling or (ii) branch-and-bound with model-based bounds. We present here a more general branch-and-bound framework that extends the possible bounds by using 'dissociation', which yields tighter bounds.
Cite
@article{arxiv.1806.10078,
title = {A General Framework for Anytime Approximation in Probabilistic Databases},
author = {Maarten Van den Heuvel and Floris Geerts and Wolfgang Gatterbauer and Martin Theobald},
journal= {arXiv preprint arXiv:1806.10078},
year = {2018}
}
Comments
3 pages, 2 figures, submitted to StarAI 2018 Workshop