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Software Comparison Dealing with Bayesian Networks

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Advances in Neural Networks – ISNN 2011 (ISNN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6677))

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Abstract

This paper presents a comparative study of tools dealing with Bayesian networks. Indeed, Bayesian networks are mathematical models now increasingly used in the field of decision support and artificial intelligence. Our study focuses on methods for inference and learning. It presents a state of the art in the field.

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References

  1. Naïm, P., Wuillemin, P.-H., Leray, P., Pourret, O., Becker, A.: Ré-seaux Bayésiens, 3rd edn. Eyrolles,

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  2. Murphy, K.: Software for Graphical models: a review. ISBA Bulletin (December 2007)

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  3. Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)

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Author information

Authors and Affiliations

  1. Preparatory Institute of Engineer of Monastir (IPEIM), 5019, Monastir, Tunisia

    Mohamed Ali Mahjoub

  2. University of Monastir (FSM), Monastir, Tunisia

    Karim Kalti

Authors
  1. Mohamed Ali Mahjoub
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  2. Karim Kalti
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Editor information

Editors and Affiliations

  1. Institute of Automation, Key Laboratory of Complex Systems and Intelligence Science, Chinese Academy of Sciences, 100190, Beijing, China

    Derong Liu

  2. College of Information Science and Engineering, Northeastern University, 110004, Shenyang, Liaoing, China

    Huaguang Zhang

  3. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios Polycarpou

  4. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  5. Deptartment of Electrical, Computer and Biomedical Engineering, University of Rhode Island, 02881, Kingston, RI, USA

    Haibo He

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© 2011 Springer-Verlag Berlin Heidelberg

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Mahjoub, M.A., Kalti, K. (2011). Software Comparison Dealing with Bayesian Networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_19

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  • DOI: https://doi.org/10.1007/978-3-642-21111-9_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21110-2

  • Online ISBN: 978-3-642-21111-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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