Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Computing marginals for arbitrary subsets from marginal representation in Markov trees
Artificial Intelligence
Some improvements to the Shenoy-Shafer and Hugin architectures for computing marginals
Artificial Intelligence
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
A sufficiently fast algorithm for finding close to optimal clique trees
Artificial Intelligence
Probabilistic Expert Systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Introduction to Algorithms
Computational Properties of Two Exact Algorithms for Bayesian Networks
Applied Intelligence
Optimal time-space tradeoff in probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on 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
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Maximal prime subgraph decomposition of Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Inference in multiply sectioned Bayesian networks: methods and performance comparison
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
A simple graphical approach for understanding probabilistic inference in Bayesian networks
Information Sciences: an International Journal
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
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We propose the first join tree (JT) propagation architecture that labels the probability information passed between JT nodes in terms of conditional probability tables (CPTs) rather than potentials. By modeling the task of inference involving evidence, we can generate three work schedules that are more time-efficient for LAZY propagation. Our experimental results, involving five real-world or benchmark Bayesian networks (BNs), demonstrate a reasonable improvement over LAZY propagation. Our architecture also models inference not involving evidence. After the CPTs identified by our architecture have been physically constructed, we show that each JT node has a sound, local BN that preserves all conditional independencies of the original BN. Exploiting inference not involving evidence is used to develop an automated procedure for building multiply sectioned BNs. It also allows direct computation techniques to answer localized queries in local BNs, for which the empirical results on a real-world medical BN are promising. Screen shots of our implemented system demonstrate the improvements in semantic knowledge.