2010 IEEE 26th International Conference on Data Engineering (ICDE 2010) (2010)
Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Dan Olteanu , Oxford University Computing Laboratory, OX1 3QD, UK
Jiewen Huang , Oxford University Computing Laboratory, OX1 3QD, UK
Christoph Koch , Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
This paper introduces a deterministic approximation algorithm with error guarantees for computing the probability of propositional formulas over discrete random variables. The algorithm is based on an incremental compilation of formulas into decision diagrams using three types of decompositions: Shannon expansion, independence partitioning, and product factorization. With each decomposition step, lower and upper bounds on the probability of the partially compiled formula can be quickly computed and checked against the allowed error.
J. Huang, C. Koch and D. Olteanu, "Approximate confidence computation in probabilistic databases," 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)(ICDE), Long Beach, CA, USA, 2010, pp. 145-156.