Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Operations Research
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Probabilistic Expert Systems
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Pruning bayesian networks for efficient computation
UAI '90 Proceedings of the Sixth 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
A join tree probability propagation architecture for semantic modeling
Journal of Intelligent Information Systems
A formal comparison of variable elimination and arc reversal in Bayesian network inference
Intelligent Decision Technologies
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Improvements to message computation in lazy propagation
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
An improved LAZY-AR approach to bayesian network inference
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Variations over the message computation algorithm of lazy propagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using four cost measures to determine arc reversal orderings
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Evaluating probabilistic inference techniques: a question of "When," not "Which"
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Answering queries in hybrid Bayesian networks using importance sampling
Decision Support Systems
Ordering arc-reversal operations when eliminating variables in lazy AR propagation
International Journal of Approximate Reasoning
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In this paper, we put forth the first join tree propagation algorithm that selectively applies either arc reversal (AR) or variable elimination (VE) to build the propagated messages. Our approach utilizes a recent method for identifying the propagated join tree messages a priori. When it is determined that a join tree node will construct a single distribution to be sent to a neighbouring node, VE is utilized as it builds a single distribution in the most direct fashion; otherwise, AR is applied as it maintains a factorization of distributions allowing for barren variables to be exploited during propagation later on in the join tree. Experimental results, involving evidence processing in four benchmark Bayesian networks, empirically demonstrate that selectively applying VE and AR is faster than applying one of these methods exclusively on the entire network.