Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Medical diagnosis using a probabilistic causal network
Applied Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
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
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
An empirical evaluation of possible variations of lazy propagation
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning Bayesian Networks
Introducing assignment functions to Bayesian optimization algorithms
Information Sciences: an International Journal
Dynamic multiagent probabilistic inference
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
A join tree probability propagation architecture for semantic modeling
Journal of Intelligent Information Systems
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Inference in hybrid Bayesian networks with mixtures of truncated exponentials
International Journal of Approximate Reasoning
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh 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
Information Sciences: an International Journal
Inference in multiply sectioned Bayesian networks: methods and performance comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Variations over the message computation algorithm of lazy propagation
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
Multi-agent neural business control system
Information Sciences: an International Journal
On the properties of concept classes induced by multivalued Bayesian networks
Information Sciences: an International Journal
Axiomatisation of fully probabilistic design
Information Sciences: an International Journal
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
An efficient node ordering method using the conditional frequency for the K2 algorithm
Pattern Recognition Letters
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We present a simple graphical method for understanding exact probabilistic inference in discrete Bayesian networks (BNs). A conditional probability table (conditional) is depicted as a directed acyclic graph involving one or more black vertices and zero or more white vertices. The probability information propagated in a network can then be graphically illustrated by introducing the black variable elimination (BVE) algorithm. We prove the correctness of BVE and establish its polynomial time complexity. Our method possesses two salient characteristics. This purely graphical approach can be used as a pedagogical tool to introduce BN inference to beginners. This is important as it is commonly stated that newcomers have difficulty learning BN inference due to intricate mathematical equations and notation. Secondly, BVE provides a more precise description of BN inference than the state-of-the-art discrete BN inference technique, called LAZY-AR. LAZY-AR propagates potentials, which are not well-defined probability distributions. Our approach only involves conditionals, a special case of potential.