Abstract
We introduce a methodology for performing approximate computations in complex probabilistic expert systems, when some components can be handled exactly and others require approximation or simulation. This is illustrated by means of a modified version of the familiar ‘chest-clinic’ problem.
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Andersen, S. K., Olesen, K. G., Jensen, F. V., Jensen, F.: HUGIN — A shell for building Bayesian belief universes for expert systems, Proceedings of the 11th International Joint Conference on Artificial Intelligence (1989) 1080–1085. Also reprinted in Shafer & Pearl (1990)
Dawid, A. P.: Applications of a general propagation algorithm for probabilistic expert systems, Statistics and Computing 2 (1992) 25–36
Gelfand, A. E., Smith, A. F. M.: Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association 85 (1990) 398–409
Gelman, A., Rubin, D. B.: Inference from iterative simulation using single and multiple sequences (with discussion), Statistical Science 7 (1992) 457–511
Geyer, C. J.: Practical Markov Chain Monte Carlo (with discussion), Statistical Science 7 (1992) 473–511
Jensen, F. V., Lauritzen, S. L., Olesen, K. G.: Bayesian updating in causal probabilistic networks by local computation, Computational Statistics Quarterly 4 (1990) 269–282
Johnson, N. L., Kotz, S.: Distributions in Statistics. Continuous Univariate Distributions, Vol. 2, Wiley & Sons, New York (1970)
Lauritzen, S. L.: Propagation of probabilities, means and variances in mixed graphical association models, Journal of the American Statistical Association 86 (1992) 1098–1108
Lauritzen, S. L., Spiegelhalter, D. J.: Local computations with probabilities on graphical structures and their application to expert systems (with discussion), Journal of the Royal Statistical Society, Series B 50 (1988) 157–224
Ripley, B. D.: Stochastic Simulation, Wiley & Sons (1987)
]Shafer, G. R., Pearl, J. (eds): Readings in Uncertain Reasoning, Morgan Kaufmann, San Mateo, California (1990)
Shenoy, P. P., Shafer, G. R.: Axioms for probability and belief-function propagation, in R. D. Shachter, T. S. Levitt, L. N. Kanal and J. F. Lemmer (eds), Uncertainty in Artificial Intelligence IV, North-Holland, Amsterdam, (1990) 169–198
Smith, A. F. M., Roberts, G. O.: Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods, Journal of the Royal Statistical Society, Series B 55(1) (1993) 5–23
Spiegelhalter, D. J., Dawid, A. P., Lauritzen, S. L., Cowell, R. G.: Bayesian analysis in expert systems (with discussion), Statistical Science 8 (1993) 219–247 and 247–283
Thomas, A., Spiegelhalter, D. J., Gilks, W. R.: BUGS: A program to perform Bayesian inference using Gibbs sampling, in J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith (eds), Bayesian Statistics 4, Clarendon Press, Oxford, UK, (1992) 837–842
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© 1995 Springer-Verlag Berlin Heidelberg
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Dawid, A.P., Kjærulff, U., Lauritzen, S.L. (1995). Hybrid propagation in junction trees. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Advances in Intelligent Computing — IPMU '94. IPMU 1994. Lecture Notes in Computer Science, vol 945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035940
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DOI: https://doi.org/10.1007/BFb0035940
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