Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computational intelligence: a logical approach
Computational intelligence: a logical approach
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Optimal time-space tradeoff in probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Query DAGs: a practical paradigm for implementing belief-network inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Conditioning graphs: practical structures for inference in bayesian networks
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Methods for constructing balanced elimination trees and other recursive decompositions
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
A conditioning graph (CG) is a graphical structure that attempt to minimize the implementation overhead of computing probabilities in belief networks. A conditioning graph recursively factorizes the network, but restricting each decomposition to a single node allows us to store the structure with minimal overhead, and compute with a simple algorithm. This paper extends conditioning graphs with optimizations that effectively reduce the height of the CG, thus reducing time complexity exponentially, while increasing the storage requirements by only a constant factor. We conclude that CGs are frequently as efficient as any other exact inference method, with the advantage of being vastly superior to VE and JT in terms of space complexity, and far simpler to implement.