Operations Research
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Cautious propagation in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Computational advantages of relevance reasoning in Bayesian belief networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Stable local computation with conditional Gaussian distributions
Statistics and Computing
Lazy Propagation and Independence of Causal Influence
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Gradient Descent Training of Bayesian Networks
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
A General Algorithm for Approximate Inference in Multiply Sectioned Bayesian Networks
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Classification using Hierarchical Naïve Bayes models
Machine Learning
Local Propagation in Conditional Gaussian Bayesian Networks
The Journal of Machine Learning Research
Maximal prime subgraph decomposition of Bayesian networks: A relational database perspective
International Journal of Approximate Reasoning
An approach to hybrid probabilistic models
International Journal of Approximate Reasoning
Belief updating in Bayesian networks by using a criterion of minimum time
Pattern Recognition Letters
Critical remarks on belief updating in Bayesian networks
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Recursive probability trees for Bayesian networks
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Lazy evaluation of symmetric Bayesian decision problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Inference in multiply sectioned Bayesian networks with extended Shafer-Shenoy and lazy propagation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Exploiting functional dependence in bayesian network inference
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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The efficiency of algorithms using secondary structures for probabilistic inference in Bayesian networks can be improved by exploiting independence relations induced by evidence and the direction of the links in the original network. In this paper we present an algorithm that on-line exploits independence relations induced by evidence and the direction of the links in the original network to reduce both time and space costs. Instead of multiplying the conditional probability distributions for the various cliques, we determine on-line which potentials to multiply when a message is to be produced. The performance improvement of the algorithm is emphasized through empirical evaluations involving large real world Bayesian networks, and we compare the method with the HUGIN and Shafer-Shenoy inference algorithms.