Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
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
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A web-based interface for hiding Bayesian network inference
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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
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 LAZY arc-reversal with variable elimination (LAZY-ARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZY-ARVE is an improvement upon LAZY arc- reversal (LAZY-AR), which was very recently proposed and empirically shown to be the state-of-the-art method for exact inference in discrete BNs. The primary advantage of LAZY-ARVE over LAZY-AR is that the former only computes the actual distributions passed during inference, whereas the latter may perform unnecessary computation by constructing irrelevant intermediate distributions. A comparison between LAZY-AR and LAZY-ARVE, involving processing evidence in a real-world BN for coronary heart disease, is favourable towards LAZY-ARVE.