Probabilistic reasoning using graphs

  • Judea Pearl
Section II Approaches To Uncertainty C) Probability Theory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 286)


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  1. 1.
    Pearl, J., “Fusion, Propagation and Structuring in Belief Networks,” Artificial Intelligence, Vol. 29, No. 3, September 1986, pp. 241–288.Google Scholar
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    Pearl, J., “How to Do with Probabilities What People Say You Can't,” Proceedings, 2nd Conference on AI Applications, Miami, FL., December 1985, pp. 6–12.Google Scholar
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    Pearl, J. & Paz, A., “GRAPHOIDS: a Graph-Based Logic for Reasoning about Relevance Relations,” Proceedings, European Conference on Artificial Intelligence-86, Brighton, U.K., June 1986.Google Scholar
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    Pearl, J., “Distributed Revision of Composite Beliefs,” Proceedings, 2nd AAAI Workshop on Uncertainty in Artificial Intelligence, Philadelphia, PA., August 1986, pp. 201–209; also to appear in Artificial Intelligence, North-Holland, 1987.Google Scholar
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    Pearl, J., “Bayes and Markov Networks: a Comparison of Two Graphical Representations of Probabilistic Knowledge,” UCLA Computer Science Department Technical Report 860024 (R-46), October 1986.Google Scholar
  6. 6.
    Geffner, H. & Pearl, J., “A Distributed Diagnosis of Systems with Multiple Faults,” Proceedings, 3rd IEEE Conference on Artificial Intelligence Applications, Orlando, Florida, February 1987, pp. 156–162.Google Scholar
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    Pearl, J., & Verma, T., "The Logic of Representing Dependencies by Directed Graphs," UCLA Cognitive Systems Laboratory Technical Report 870004 (R-79), February 1987, Proceedings, AAAI Conference, Seattle, WA. July, 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Judea Pearl
    • 1
  1. 1.Cognitive Systems LaboratoryUCLA Computer Science DepartmentLos Angeles

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