Optimizing Inference in Bayesian Networks and Semiring Valuation Algebras

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)


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.


Bayesian Network Boolean Function Local Computation Fusion Algorithm Inference Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.University of BernSwitzerland
  2. 2.Bern University of Applied SciencesSwitzerland
  3. 3.University of FribourgSwitzerland

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