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Dissociation and Propagation for Approximate Lifted Inference with Standard Relational Database Management Systems
[article]
2016
arXiv
pre-print
Probabilistic inference over large data sets is a challenging data management problem since exact inference is generally #P-hard and is most often solved approximately with sampling-based methods today. This paper proposes an alternative approach for approximate evaluation of conjunctive queries with standard relational databases: In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true
arXiv:1310.6257v4
fatcat:ematfjcsdbavzd7siv3q2bikza