Skip to main content
Log in

Stable local computation with conditional Gaussian distributions

  • Published:
Statistics and Computing Aims and scope Submit manuscript

Abstract

This article describes a propagation scheme for Bayesian networks with conditional Gaussian distributions that does not have the numerical weaknesses of the scheme derived in Lauritzen (Journal of the American Statistical Association 87: 1098–1108, 1992).

The propagation architecture is that of Lauritzen and Spiegelhalter (Journal of the Royal Statistical Society, Series B 50: 157– 224, 1988).

In addition to the means and variances provided by the previous algorithm, the new propagation scheme yields full local marginal distributions. The new scheme also handles linear deterministic relationships between continuous variables in the network specification.

The computations involved in the new propagation scheme are simpler than those in the previous scheme and the method has been implemented in the most recent version of the HUGIN software.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Cowell R.G., Dawid A.P., Lauritzen S.L., and Spiegelhalter D.J. 1999. Probabilistic Networks and Expert Systems. Springer-Verlag, New York.

    Google Scholar 

  • Jensen F.V. 1996. An Introduction to Bayesian Networks. University College London Press, London.

    Google Scholar 

  • Jensen F.V., Lauritzen S.L., and Olesen K.G. 1990. Bayesian updating in causal probabilistic networks by local computation. Computational Statistics Quarterly 4: 269-282.

    Google Scholar 

  • Lauritzen S.L. 1992. Propagation of probabilities, means and variances in mixed graphical association models. Journal of the American Statistical Association 87: 1098-1108.

    Google Scholar 

  • Lauritzen S.L. 1996. Graphical Models. Clarendon Press, Oxford.

    Google Scholar 

  • Lauritzen S.L. and Jensen F.V. 1997. Local computation with valuations from a commutative semigroup. Annals of Mathematics and Artificial Intelligence 21: 51-69.

    Google Scholar 

  • Lauritzen S.L. and Spiegelhalter D.J. 1988.Local computations with probabilities on graphical structures and their application to expert systems (with discussion). Journal of the Royal Statistical Society, Series B 50: 157-224.

    Google Scholar 

  • Lauritzen S.L. and Wermuth N. 1984. Mixed interaction models. Technical Report R 84-8, Institute for Electronic Systems, Aalborg University.

    Google Scholar 

  • Lauritzen S.L. and Wermuth N. 1989. Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics 17: 31-57.

    Google Scholar 

  • Madsen A.L. and Jensen F.V. 1998. Lazy propagation in junction trees. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Cooper G.F. and Moral S. (Eds.), Morgan Kaufmann, San Mateo, California, pp. 362-369.

    Google Scholar 

  • Pearl J. 1986. Fusion, propagation, and structuring in belief networks. Artificial Intelligence 29: 241-288.

    Google Scholar 

  • Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, California.

    Google Scholar 

  • Penrose R. 1955. A generalized inverse for matrices. Proceedings of the Cambridge Philosophical Society 51: 406-413.

    Google Scholar 

  • Rao C.R. 1973. Linear Statistical Inference and Its Applications, 2 edn. John Wiley and Sons, New York.

    Google Scholar 

  • Rao C.R. and Mitra S.K. 1971. Generalized Inverse of Matrices and Its Applications. John Wiley and Sons, New York.

    Google Scholar 

  • Shafer G. 1991. An axiomatic study of computation in hypertrees. Technical Report WP-232, School of Business, University of Kansas.

    Google Scholar 

  • Shafer G. 1996. Probabilistic Expert Systems. Society for Industrial and Applied Mathematics, Philadelphia.

    Google Scholar 

  • Shenoy P.P. and Shafer G. 1990. Axioms for probability and belieffunction propagation. In: Shachter R.D., Levitt T.S., Kanal L.N., and Lemmer J.F. (Eds.), Uncertainty in Artificial Intelligence, Vol. 4. North-Holland, Amsterdam, The Netherlands, pp. 169-198.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lauritzen, S.L., Jensen, F. Stable local computation with conditional Gaussian distributions. Statistics and Computing 11, 191–203 (2001). https://doi.org/10.1023/A:1008935617754

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008935617754

Navigation