Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Probabilistic Expert Systems
An empirical evaluation of possible variations of lazy propagation
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
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
Join tree propagation utilizing both arc reversal and variable elimination
International Journal of Approximate Reasoning
Variations over the message computation algorithm of lazy propagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evaluating probabilistic inference techniques: a question of "When," not "Which"
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Ordering arc-reversal operations when eliminating variables in lazy AR propagation
International Journal of Approximate Reasoning
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Four cost measures s1, s2, s3, s4 were recently studied for sorting the operations in Lazy propagation with arc reversal (LPAR), a join tree propagation approach to Bayesian network inference. It has been suggested to use s1 with LPAR, since there is an effectiveness ranking, say s1, s2, s3, s4, when applied in isolation. In this paper, we also suggest to use s1 with LPAR, but to use s2 to break s1 ties, s3 to break s2 ties, and s4 to break s3 ties. Experimental results show that sometimes there is a noticeable gain to be made.