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Advanced Inference in Bayesian Networks

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Learning in Graphical Models

Part of the book series: NATO ASI Series ((ASID,volume 89))

Abstract

The previous chapter introduced inference in discrete variable Bayesian networks. This used evidence propagation on the junction tree to find marginal distributions of interest. This chapter presents a tutorial introduction to some of the various types of calculations which can also be performed with the junction tree, specifically:

  • Sampling.

  • Most likely configurations.

  • Fast retraction.

  • Gaussian and conditional Gaussian models.

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References

  • Buntine, W. L. (1994). Operations for learning with graphical models. Journal of Artificial Intelligence Research, 2, pp. 159–225.

    Google Scholar 

  • Cowell, R. G. (1997). Sampling without replacement in junction trees, Research Report 15, Department of Actuarial Science and Statistics, City University, London.

    Google Scholar 

  • Cowell, R. G. and Dawid, A. P. (1992). Fast retraction of evidence in a probabilistic expert system. Statistics and Computing, 2, pp. 37–40.

    Article  Google Scholar 

  • Dawid, A. P. (1992). Applications of a general propagation algorithm for probabilistic expert systems. Statistics and Computing, 2, pp. 25–36.

    Article  Google Scholar 

  • Henrion, M. (1988). Propagation of uncertainty by probabilistic logic sampling in Bayes’ networks. In Uncertainty in Artificial Intelligence, (ed J Lemmer and L. N. Kanal ), pp. 149–64. North-Holland, Amsterdam.

    Google Scholar 

  • Jensen, F., Jensen, F. V., and Dittmer, S. L. (March 1994). From influence diagrams to junction trees. Technical Report R-94–2013, Department of Mathematics and Computer Science Aalborg University, Denmark.

    Google Scholar 

  • Kjærulff, U. (1993). A computational scheme for reasoning in dynamic probabilistic networks. Research Report R-93–2018, Department of Mathematics and Computer Science, Aalborg University, Denmark.

    Google Scholar 

  • Lauritzen, S. L. (1992). Propagation of probabilities, means and variances in mixed graphical association models. Journal of the American Statistical Association, 87, pp. 1098–108.

    Article  MathSciNet  MATH  Google Scholar 

  • Nilsson, D. (1994). An algorithm for finding the most probable configurations of discrete variables that are specified in probabilistic expert systems. M.Sc. Thesis, Department of Mathematical Statistics, University of Copenhagen.

    Google Scholar 

  • Nilsson, D. (1997). An efficient algorithm for finding the M most probable configurations in a probabilistic expert system. Submitted to Statistics and Computing.

    Google Scholar 

  • Shachter, R. D., Andersen, S. K., and Szolovits, P. (1994). Global conditioning for probabilistic inference in belief networks. In Proceedings of the Tenth Conference on Uncertainty in Artifical Intelligence, pp 514–522.

    Google Scholar 

  • Shachter, R. and Kenley, C. (1989). Gaussian influence diagrams. Management Science, 35, pp. 527–50.

    Article  Google Scholar 

  • Shachter, R. and Peot, M. (1989). Simulation approaches to general probabilistic inference on belief networks. In Uncertainty in Artificial Intelligence5, (ed. M. Hennon, R. D. Shachter, L. Kanal, and J. Lemmer), pp. 221–31. North-Holland, North-Holland.

    Google Scholar 

  • Smith, J. Q., French, S., and Raynard, D. (1995). An efficient graphical algorithm for updating the estimates of the dispersal of gaseous waste after an accidental release. In Probabilistic reasoning and Bayesian belief networks, (ed. A. Gammerman ), pp. 125–44. Alfred Waller, Henley-on-Thames.

    Google Scholar 

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© 1998 Springer Science+Business Media Dordrecht

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Cowell, R. (1998). Advanced Inference in Bayesian Networks. In: Jordan, M.I. (eds) Learning in Graphical Models. NATO ASI Series, vol 89. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5014-9_2

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  • DOI: https://doi.org/10.1007/978-94-011-5014-9_2

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6104-9

  • Online ISBN: 978-94-011-5014-9

  • eBook Packages: Springer Book Archive

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