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From Information to Evidence in a Bayesian Network

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Probabilistic Graphical Models (PGM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8754))

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Abstract

Evidence in a Bayesian network comes from information based on the observation of one or more variables. A review of the terminology leads to the assessment that two main types of non-deterministic evidence have been defined, namely likelihood evidence and probabilistic evidence but the distinction between fixed probabilistic evidence and not fixed probabilistic evidence is not clear, and neither terminology nor concepts have been clearly defined. In particular, the term soft evidence is confusing. The article presents definitions and concepts related to the use of non-deterministic evidence in Bayesian networks, in terms of specification and propagation. Several examples help to understand how an initial piece of information can be specified as a finding in a Bayesian network.

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Author information

Authors and Affiliations

  1. LAMIH UMR 8201, University Lille Nord de France, F-59313, Valenciennes, France

    Ali Ben Mrad, Véronique Delcroix & Sylvain Piechowiak

  2. ENIS, CES Lab, Université de Sfax, 3038, Sfax, Tunisie

    Ali Ben Mrad

  3. Centre for Renewable Energy Systems Technology, Loughborough University, UK

    Philip Leicester

Authors
  1. Ali Ben Mrad
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  2. Véronique Delcroix
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  3. Sylvain Piechowiak
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  4. Philip Leicester
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Editor information

Editors and Affiliations

  1. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht, The Netherlands

    Linda C. van der Gaag

  2. Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht,, The Netherlands

    Ad J. Feelders

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Ben Mrad, A., Delcroix, V., Piechowiak, S., Leicester, P. (2014). From Information to Evidence in a Bayesian Network. In: van der Gaag, L.C., Feelders, A.J. (eds) Probabilistic Graphical Models. PGM 2014. Lecture Notes in Computer Science(), vol 8754. Springer, Cham. https://doi.org/10.1007/978-3-319-11433-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-11433-0_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11432-3

  • Online ISBN: 978-3-319-11433-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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