Skip to main content
Springer Nature Link
Log in

Tutorial and Selected Approaches on Parameter Learning in Bayesian Network with Incomplete Data

  • Conference paper
Advances in Neural Networks – ISNN 2012 (ISNN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7367))

Included in the following conference series:

  • 2762 Accesses

Abstract

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. This paper presents a tutorial of basic concepts and in particular techniques and algorithms associated with learning in Bayesian network with incomplete data. We present also selected applications in the fields.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  2. Feelders, A.D.: A new parameter learning method for bayesian network with qualitative influences. In: UAI, pp. 117–124 (2007)

    Google Scholar 

  3. Feelders, A.D., Van der Gaag, L.: Learning bayesian network parameters with prior knowledge about context-specific qualitative influences. In: Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp. 193–200. AUAI Press, Arlington (2005)

    Google Scholar 

  4. Feelders, A.D.: Learning bayesian network under order constraints. In: Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp. 37–53 (2006)

    Google Scholar 

  5. Yonghui, C.: Study of the case of learning bayesian network from incomplete data. In: International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII, vol. 4, pp. 66–69 (December 2009)

    Google Scholar 

  6. de Campos, C.P.: Improving bayesian network parameter learning using constraints. In: International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)

    Google Scholar 

  7. Jensen, F.V., Lauritzen, S.L., Olesen, K.G.: Bayesian updating in recursive graphical models by local computations. Computational Statistical Quaterly 4, 269–282 (1990)

    MathSciNet  Google Scholar 

  8. Celeux, G., Diebolt, J.: The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Comp. Statis. Quaterly 2, 73–82 (1985)

    Google Scholar 

  9. Celeux, G., Govaert, G.: A classification EM algorithm for clustering and two stochastic versions. Computational Statistics and Data Analysis 14(3), 315–332 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  10. Ramoni, M., Sebastiani, P.: Robust Learning with Missing Data. Machine Learning 45, 147–170 (2001)

    Article  MATH  Google Scholar 

  11. Ramoni, M., Sebastiani, P.: Robust Learning with Missing Data. Machine Learning 45, 147–170 (2001)

    Article  MATH  Google Scholar 

  12. Friedman, N.: The Bayesian structural EM algorithm. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 129–138. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  13. Neal, R.M., Hinton, G.E.: A new view of the EM algorithm that justifies incremental, sparse and other variants. In: Learning in Graphical Models, pp. 355–368. Kluwer Academic Publishers (1998)

    Google Scholar 

  14. Niculescu, R.S., Mitchell, T.M.: Bayesian network learning with parameter constraints. Journal of Machine Learning Research 7, 1357–1383 (2006)

    MathSciNet  MATH  Google Scholar 

  15. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence (6), 721–741 (1984)

    Google Scholar 

  16. Lauritzen, S.L.: The EM algorithm for graphical association models with missing data. Computational Statistics and Data Analysis 19, 191–201 (1991)

    Article  Google Scholar 

  17. Liao, W., Ji, Q.: Exploiting qualitative domain knowledge for learning bayesian network parameters with incomplete data. U.S. Army Research Office under grant number W911NF-06-1-0331 (2009)

    Google Scholar 

  18. Liao, W., Ji, Q.: Learning bayesian network parameters under incomplete data with domain knowledge. Pattern Recognition 42, 3046–3056 (2009)

    Article  MATH  Google Scholar 

  19. Xiang, Y., Pant, B., Eisen, A., Beddoes, M.P., Poole, D.: Multiply sectioned Bayesian networks for neuromuscular diagnosis. In: PubMed., pp. 293–314 (1993)

    Google Scholar 

  20. Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Bayesian Analysis in Probabilistic Networks. Springer (1999)

    Google Scholar 

  21. Lim, S., Cho, S.-B.: Online Learning of Bayesian Network Parameters with Incomplete Data (2004)

    Google Scholar 

  22. Niculescu, R.S., Mitchell, T.M.: Bayesian Network Learning with Parameter Constraints. Journal of Machine Learning Research 7, 1357–1383 (2006)

    MathSciNet  MATH  Google Scholar 

  23. Liao, W.: Learning Bayesian Network Parameters Under Incomplete Data with Domain Knowledge (2007)

    Google Scholar 

  24. de Campos, C.P., Ji, Q.: Improving Bayesian Network Parameter Learning using Constraints. IEEE (2008)

    Google Scholar 

  25. Wittig, F., Jameson, A.: Exploiting qualitative knowledge in the learning of conditional probabilities of Bayesian networks. In: UAI, pp. 644–652 (2000)

    Google Scholar 

  26. Niculescu, R.S., Mitchell, T.M., Rao, R.B.: A theoretical framework for learning Bayesian networks with parameter inequality constraints. In: IJCAI (2007)

    Google Scholar 

  27. Jaeger, M.: The AI&M procedure for learning from incomplete data. In: Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, pp. 225–232 (2006)

    Google Scholar 

  28. Elidan, G., Friedman, N.: The information bottleneck EM algorithm. In: Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence, pp. 200–209 (2003)

    Google Scholar 

  29. Lamine, F.B., Kalti, K., Mahjoub, M.A.: The threshold EM algorithm for parameter learning in bayesian network with incomplete data. (IJACSA) International Journal of Advanced Computer Science and Applications 2(7), 86–90 (2011)

    Google Scholar 

  30. Mahjoub, M.A., Kalti, K.: Software Comparison Dealing with Bayesian Networks. In: Liu, D. (ed.) ISNN 2011, Part III. LNCS, vol. 6677, pp. 168–177. Springer, Heidelberg (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Preparatory Institute of Engineer of Monastir, Street Ibn Eljazzar, 5019, Monastir, Tunisia

    Mohamed Ali Mahjoub

  2. Sage (Advanced Systems in Electrical Engineering), National School of Engineering of Sousse, Tunisia

    Mohamed Ali Mahjoub, Abdessalem Bouzaiene & Nabil Ghanmy

Authors
  1. Mohamed Ali Mahjoub
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdessalem Bouzaiene
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nabil Ghanmy
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical & Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Jun Wang

  2. School of Electrical and Computer Engineering, Oklahoma State University, 74078, Stillwater, OK, USA

    Gary G. Yen

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

    Marios M. Polycarpou

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mahjoub, M.A., Bouzaiene, A., Ghanmy, N. (2012). Tutorial and Selected Approaches on Parameter Learning in Bayesian Network with Incomplete Data. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31346-2_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31345-5

  • Online ISBN: 978-3-642-31346-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics