Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the generation of alternative explanations with implications for belief revision
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A new algorithm for finding MAP assignments to belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Empirical evaluation of approximation algorithms for probabilistic decoding
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A scheme for approximating probabilistic inference
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
New Search Heuristics for Max-CSP
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Mini-bucket heuristics for improved search
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
The paper describes a branch and bound scheme that uses heuristics generated mechanically by the mini-bucket approximation. This scheme is presented and evaluated for optimization tasks such as finding the Most Probable Explanation (MPE) in Bayesian networks. The mini-bucket scheme yields monotonic heuristics of varying strengths which cause different amounts of pruning, allowing a controlled tradeoff between preprocessing and search. The resulting Branch and Bound with Mini-Bucket heuristic (BBMB), is evaluated using random networks, probabilistic decoding and medical diagnosis networks. Results show that the BBMB scheme overcomes the memory explosion of bucket-elimination allowing a gradual tradeoff of space for time, and of time for accuracy.