Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic inference and influence diagrams
Operations Research
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
International Journal of Approximate Reasoning
Multiplicative factorization of noisy-max
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
Real-time inference with large-scale temporal bayes nets
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Any-space probabilistic inference
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Context-specific approximation in probabilistic inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Query DAGs: a practical paradigm for implementing belief-network inference
UAI'96 Proceedings of the Twelfth international 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
Backward simulation in Bayesian networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Incremental dynamic construction of layered polytree networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Incremental probabilistic inference
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
An efficient approach for finding the MPE in belief networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Parallelizing probabilistic inference: some early explorations
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
Integrating model construction and evaluation
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
Sidestepping the triangulation problem in Bayesian net computations
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
Symbolic probabilistic inference with continuous variables
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Symbolic probabilistic inference with evidence potential
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Local expression languages for probabilistic dependence: a preliminary report
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Diagnosis from bayesian networks with fuzzy parameters – a case in supply chains
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
A search problem in complex diagnostic Bayesian networks
Knowledge-Based Systems
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
Importance sampling algorithms for Bayesian networks: Principles and performance
Mathematical and Computer Modelling: An International Journal
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
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The Symbolic Probabilistic Inference (SPI) Algorithm [D'Ambrosio, 1989] provides an efficient framework for resolving general queries on a belief network. It applies the concept of dependency-directed backward search to probabilistic inference, and is incremental with respect to both queries and observations. Unlike most belief network algorithms, SPI is goal directed, performing only those calculations that are required to respond to queries. The directed graph of the underlying belief network is used to develop a tree structure for recursive query processing. This allows effective caching of intermediate results and significant opportunities for parallel computation. A simple preprocessing step ensures that, given the search tree, the algorithm will include no unnecessary distributions. The preprocessing step eliminates dimensions from the intermediate results and prunes the search path.