Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Probabilistic Expert Systems
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
An empirical evaluation of possible variations of lazy propagation
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Improvements to message computation in lazy propagation
International Journal of Approximate Reasoning
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Lazy evaluation of symmetric Bayesian decision problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A hybrid algorithm to compute marginal and joint beliefs in Bayesian networks and its complexity
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Maximal prime subgraph decomposition of Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Variations over the message computation algorithm of lazy propagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
All roads lead to Rome---New search methods for the optimal triangulation problem
International Journal of Approximate Reasoning
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
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Lazy Propagation (LP) is a propagation scheme for belief update in Bayesian networks based upon Shenoy-Shafer propagation. So far the secondary computational structure has been a junction tree (or strong junction tree). This paper describes and shows how different tree structures can be used for LP. This includes the use of different junction trees and the maximal prime subgraph decomposition organised as a tree. The paper reports on the results of an empirical evaluation on a set of real-world Bayesian networks of the performance impact of using different tree structures in LP. The results indicate that the tree structure can have a significant impact on both time and space performance of belief update.