Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
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Axioms for probability and belief-function propagation
Readings in uncertain reasoning
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Artificial Intelligence - Special issue on relevance
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Artificial Intelligence
Branching programs and binary decision diagrams: theory and applications
Branching programs and binary decision diagrams: theory and applications
Axioms for probability and belief-function proagation
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Information Algebras: Generic Structures for Inference
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Journal of Artificial Intelligence Research
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Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
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UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Previous work on context-specific independence in Bayesian networks is driven by a common goal, namely to represent the conditional probability tables in a most compact way. In this paper, we argue from the view point of the knowledge compilation map and conclude that the language of Ordered Binary Decision Diagrams (OBDD) is the most suitable one for representing probability tables, in addition to the language of Algebraic Decision Diagrams (ADD). We thus suggest the replacement of the current practice of using tree-based or rule-based representations. This holds not only for inference in Bayesian networks, but is more generally applicable in the generic framework of semiring valuation algebras, which can be applied to solve a variety of inference and optimization problems in different domains.