Communications of the ACM - Special issue on parallelism
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the parallel complexity of discrete relaxation in constraint satisfaction networks
Artificial Intelligence
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Parallel algorithms for shared-memory machines
Handbook of theoretical computer science (vol. A)
Dynamic expression trees and their applications
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Local consistency in parallel constraint satisfaction networks
Artificial Intelligence
A data structure for dynamically maintaining rooted trees
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Optimal Parallel Evaluation of Tree-Structured Computations by Raking
AWOC '88 Proceedings of the 3rd Aegean Workshop on Computing: VLSI Algorithms and Architectures
Parallel tree contraction and its application
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
ACM Computing Surveys (CSUR)
Efficient sequential clamping for lifted message passing
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Logarithmic time parallel Bayesian inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Adaptive Exact Inference in Graphical Models
The Journal of Machine Learning Research
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Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic databases with comparable efficiency. We propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks. In the conventional algorithm, new evidence is absorbed in time O(1) and queries are processed in time O(N), where N is the size of the network. We propose an algorithm which, after a preprocessing phase, allows us to answer queries in time O(log N) at the expense of O(log N) time per evidence absorption. The usefulness of sub-linear processing time manifests itself in applications requiring (near) real-time response over large probabilistic databases. We briefly discuss a potential application of dynamic probabilistic reasoning in computational biology.