Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Approximating probabilistic inference in Bayesian belief networks is NP-hard
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
Improved decision-making in game trees: recovering from pathology
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Probabilistic prediction of protein secondary structure using causal networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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In this paper we propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks (causal trees and polytrees). 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 a practical 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.