Probabilistic Inference in Artificial Intelligence: The Method of Bayesian Networks

  • Jean-Louis Golmard
Part of the Philosophical Studies Series book series (PSSP, volume 56)


Bayesian networks are formalisms which associate a graphical representation of causal relationships and an associated probabilistic model. They allow to specify easily a consistent probabilistic model from a set of local conditional probabilities. In order to infer the probabilities of some facts, given observations, inference algorithms have to be used, since the size of the probabilistic models is usually large. Several such inference methods are described and illustrated. Less advanced related problems, namely learning, validation, continuous variables, and time, are briefly discussed. Finally, the relationships between the field of Bayesian networks and other scientific domains are reviewed.


Conditional Probability Probabilistic Model Bayesian Network Maximal Clique Marginal Probability 
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 Science+Business Media Dordrecht 1993

Authors and Affiliations

  • Jean-Louis Golmard
    • 1
  1. 1.Département de BiomathématiquesParisFrance

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