Operations Research
Management Science
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Stable local computation with conditional Gaussian distributions
Statistics and Computing
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Learning Bayesian Networks
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A simple graphical approach for understanding probabilistic inference in Bayesian networks
Information Sciences: an International Journal
A web-based interface for hiding Bayesian network inference
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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
Evaluating probabilistic inference techniques: a question of "When," not "Which"
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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
Ordering arc-reversal operations when eliminating variables in lazy AR propagation
International Journal of Approximate Reasoning
On the tree structure used by lazy propagation for inference in bayesian networks
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
| Hi-index | 0.02 |
As real-world Bayesian networks continue to grow larger and more complex, it is important to investigate the possibilities for improving the performance of existing algorithms of probabilistic inference. Motivated by examples, we investigate the dependency of the performance of Lazy propagation on the message computation algorithm. We show how Symbolic Probabilistic Inference (SPI) and Arc-Reversal (AR) can be used for computation of clique to clique messages in the addition to the traditional use of Variable Elimination (VE). In addition, the paper presents the results of an empirical evaluation of the performance of Lazy propagation using VE, SPI, and AR as the message computation algorithm. The results of the empirical evaluation show that for most networks, the performance of inference did not depend on the choice of message computation algorithm, but for some randomly generated networks the choice had an impact on both space and time performance. In the cases where the choice had an impact, AR produced the best results.