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
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
Computational Properties of Two Exact Algorithms for Bayesian Networks
Applied 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 Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
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
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
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
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Historically, it has been claimed that one inference algorithm or technique, say A, is better than another, say B, based on the running times on a test set of Bayesian networks. Recent studies have instead focusing on identifying situations where A is better than B, and vice versa. We review two cases where competing inference algorithms (techniques) have been successfully applied together in unison to exploit the best of both worlds. Next, we look at recent advances in identifying structure and semantics. Finally, we present possible directions of future work in exploiting structure and semantics for faster probabilistic inference.