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Probabilistic Inference in Artificial Intelligence: The Method of Bayesian Networks

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Philosophy of Probability

Part of the book series: Philosophical Studies Series ((PSSP,volume 56))

Abstract

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.

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Author information

Authors and Affiliations

  1. Département de Biomathématiques, Paris, France

    Jean-Louis Golmard

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  1. Jean-Louis Golmard
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Editors and Affiliations

  1. University of Paris I — Sorbonne and C.N.R.S., France

    Jacques-Paul Dubucs

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© 1993 Springer Science+Business Media Dordrecht

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Golmard, JL. (1993). Probabilistic Inference in Artificial Intelligence: The Method of Bayesian Networks. In: Dubucs, JP. (eds) Philosophy of Probability. Philosophical Studies Series, vol 56. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8208-7_11

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  • DOI: https://doi.org/10.1007/978-94-015-8208-7_11

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-4301-6

  • Online ISBN: 978-94-015-8208-7

  • eBook Packages: Springer Book Archive

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