Evidence Propagation

Chapter
Part of the Texts in Computer Science book series (TCS)

Abstract

After having discussed efficient representations for expert and domain knowledge, we intent to exploit them to draw inferences when new information (evidence) becomes known. The objective is to propagate the evidence through the underlying network to reach all relevant attributes. Obviously, the graph structure will play an important role.

Keywords

Summing 

References

  1. C. Borgelt, M. Steinbrecher, R. Kruse, Graphical Models – Representations for Learning, Reasoning and Data Mining, 2nd edn. (Wiley, Chichester, 2009)Google Scholar
  2. E. Castillo, J.M. Gutiérrez, A.S. Hadi, Expert Systems and Probabilistic Network Models (Springer, New York, 1997)Google Scholar
  3. R. Dechter, Bucket elimination: a unifying framework for probabilistic inference, in Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (UAI’96, Portland, OR, USA), pp. 211–219. Morgan Kaufmann, San Mateo, CA, USA (1996)Google Scholar
  4. F.V. Jensen, An Introduction to Bayesian Networks (UCL Press, London, 1996)Google Scholar
  5. F.V. Jensen, Bayesian Networks and Decision Graphs (Springer, Berlin, 2001)Google Scholar
  6. F.V. Jensen, T.D. Nielsen, Bayesian Networks and Decision Graphs, 2nd edn. (Springer, London, 2007)Google Scholar
  7. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, San Mateo, 1988)Google Scholar
  8. N.L. Zhang, D. Poole. Exploiting causal independence in bayesian network inference. J. Artif. Intell. Res. 5:301–328 (1996). (Morgan Kaufmann, San Mateo, CA, USA)Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.Otto-von-Guericke-University MagdeburgMagdeburgGermany
  2. 2.SAP Innovation CenterPotsdamGermany

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