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Bayesian Networks

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Book cover Encyclopedia of Optimization

Article Outline

Keywords

Synonyms

Introduction

Definitions

  The Chain Rule for Bayesian Network

  Cases/Models

  Methods

  Applications

See also

References

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References

  1. Andreassen S (1992) Knowledge representation by extended linear models. In: Keravnou E (ed) Deep Models for Medical Knowledge Engineering. Elsevier, pp 129–145

    Google Scholar 

  2. Bangsø O, Wuillemin PH (2000) Top-down Construction and Repetitive Structures Representation in Bayesian Networks, Proceedings of the Thirteenth International FLAIRS Conference. AIII Press, Cambridge, MA

    Google Scholar 

  3. Cano R, Sordo C, Gutierrez JM (2004) Applications of Bayesian Networks in Meteorology, Advances in Bayesian Networks. In: Gamez et al (eds) Springer, pp 309–327

    Google Scholar 

  4. de Dombal F, Leaper D, Staniland J, McCan A, Harrocks J (1972) Computer-aided diagnostics of acute abdominal pain. Brit Med J 2:9–13

    Google Scholar 

  5. Etxerberria R, Larrañaga P (1999) Global optimization with Bayesian networks, II Symposium on Artificial Intelligence, CIMAF-99. Special Session on Distribution and Evolutionary Optimization. ICIMAF, La Habana, Cuba, pp 332–339

    Google Scholar 

  6. Gneiting T, Raftery AE (2005) Strictly proper scoring rules, prediction, and estimation, Technical Report no. 463R. Department of Statistics, University of Washington

    Google Scholar 

  7. Heckerman D, Horvitz E, Nathwani B (1992) Towards normative expert systems: Part I, the Pathfinder project. Method Inf Med 31:90–105

    Google Scholar 

  8. Jensen FV (1996) An Introduction to Bayesian Networks. UCL Press, London

    Google Scholar 

  9. Jensen FV (1999) Gradient descent training of Bayesian networks, Proceedings of the Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU). Springer, Berlin, pp 190–200

    Google Scholar 

  10. Kjærulff U (1995) HUGS: Combining exact inference and Gibbs sampling in junction trees, Proceedings of the Eleventh Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, pp 368–375

    Google Scholar 

  11. Larrañaga P, Etxeberria R, Lozano JA, Peña JM (1999) Optimization by learning and simulation of Bayesian and Gaussian networks, Technical Report EHU-KZAA-IK-4/99. Department of Computer Science and Artificial Intelligence, University of the Basque Country

    Google Scholar 

  12. Lauritzen SL (1996) Graphical Models. Oxford University Press, Oxford

    Google Scholar 

  13. Livescu K, Glass J, Bilmes J (2003) Hidden feature modeling for speech recognition using dynamic Bayesian networks. Proc. EUROSPEECH, Geneva Switzerland, August–September

    Google Scholar 

  14. Mittal A, Kassim A, Tan T (2007) Bayesian Network Technologies: Applications and Graphical Models, Interface Graphics, Inc., Minneapolis, USA

    Google Scholar 

  15. Nefian AV, Liang L, Pi X, Liu X, Murphy K (2002) Dynamic Bayesian Networks for Audio-visual Speech Recognition. J Appl Signal Proc 11:1–15

    Google Scholar 

  16. Olesen KG, Lauritzen SL, Jensen FV (1992) aHUGIN: A system creating adaptive causal probabilistic networks, Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, pp 223–229

    Google Scholar 

  17. Pearl J (1982) Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach, National Conference on Artificial Intelligence. AAAI Press, Menlo Park, CA, pp 133–136

    Google Scholar 

  18. Pearl J (1986) Fusion, propagation, and structuring in belief networks. Artif Intell 29(3):241–288

    Article  MathSciNet  MATH  Google Scholar 

  19. Pearl J (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference Series in Representation and Reasoning. Morgan Kaufmann, San Francisco

    Google Scholar 

  20. Pelikan M, Goldberg DE, Cantú-Paz E (1999) BOA: The Bayesian Optimization Algorithm, Proceedings of the Genetic and Evolutionary Computation conference GECCO-99, vol 1. Morgan Kaufmann, San Francisco

    Google Scholar 

  21. Spiegelhalter DJ, Knill-Jones RP (1984) Statistical and knowledge-based approaches to clinical decision-support systems. J Royal Stat Soc A147:35–77

    Google Scholar 

  22. Spiegelhalter D, Lauritzen SL (1990) Sequential updating of conditional probabilities on directed graphical structures. Networks 20:579–605

    Article  MathSciNet  MATH  Google Scholar 

  23. Vomlel J (2003) Two applications of Bayesian networks, Proceedings of conference Znalosti. Ostrava, Czech Republic, pp 73–82

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA

    Alla R. Kammerdiner

Authors
  1. Alla R. Kammerdiner
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Editor information

Editors and Affiliations

  1. Department of Chemical Engineering, Princeton University, Princeton, NJ, 08544-5263, USA

    Christodoulos A. Floudas

  2. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611-6595, USA

    Panos M. Pardalos

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© 2008 Springer-Verlag

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Kammerdiner, A.R. (2008). Bayesian Networks . In: Floudas, C., Pardalos, P. (eds) Encyclopedia of Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74759-0_32

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