Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A munin network for the median nerve-a case study on loops
Applied Artificial Intelligence
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
A Tractable Inference Algorithm for Diagnosing Multiple Diseases
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Performing incremental Bayesian inference by dynamic model counting
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
A structure-based variable ordering heuristic for SAT
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Multiplicative factorization of noisy-max
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Heuristics for fast exact model counting
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Causal independence for probability assessment and inference using Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
Previous studies have demonstrated that encoding a Bayesian network into a SAT-CNF formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference in Bayesian networks. In this paper, we present techniques for improving this approach for Bayesian networks with noisy-OR and noisy-MAX relations-two relations which are widely used in practice as they can dramatically reduce the number of probabilities one needs to specify. In particular, we present two space efficient CNF encodings for noisy-OR/MAX and explore alternative search ordering heuristics. We experimentally evaluated our techniques on large-scale real and randomly generated Bayesian networks. On these benchmarks, our techniques gave speedups of up to two orders of magnitude over the best previous approaches and scaled up to networks with larger numbers of random variables.