# Article

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Keywords:
graphical probabilistic models; probabilistic inference; marginal problem
Summary:
The paper deals with practical aspects of decision making under uncertainty on finite sets. The model is based on marginal problem. Numerical behaviour of 10 different algorithms is compared in form of a study case on the data from the field of rheumatology. (Five of the algorithms types were suggested by A. Perez.) The algorithms (expert systems, inference engines) are studied in different situations (combinations of parameters).
References:
[1] Cheeseman P.: A method of computing generalized Bayesian probability values of expert systems with probabilistic background. In: Proc. 6th Joint Conf. on AI(IJCAI-83), Karlsruhe
[2] Deming W. E., Stephan F. F: On a least square adjustment of sampled frequency table when expected marginal totals are known. Ann. Math. Stat. 11 (1940), 427–444 MR 0003527
[3] Perez A.: $\varepsilon$-admissible simplifications of the dependence structure of random variables. Kybernetika 13 (1979), 439–449 MR 0472224
[5] Jaynes E. T.: On the rationale of maximum-entropy methods. Proc. IEEE 70 (1980), 939–952
[6] Jiroušek R., Perez, A., Kříž O.: Intensional way of knowledge integration for expert systems. In: DIS’88 – Distributed Intelligence Systems, Varna 1988, pp. 219–227
[7] Kellerer H. G.: Verteilungsfunktionen mit gegebenen Marginalverteilungen. Z. Wahrsch. verw. Gebiete 3 (1964), 247–270 MR 0175158 | Zbl 0126.34003
[8] Kříž O.: A new algorithm for decision making with probabilistic background In: Trans. Eleventh Prague Conference on Information Theory, Statistical Decision Functions and Random Processes, Vol. B, Prague 1990, Academia, Prague 1992, pp. 135–143
[9] Kříž O.: Optimizations on finite-dimensional distributions with fixed marginals. In: WUPES 94, Proc. Third Workshop on Uncertainty Processing (R. Jiroušek, ed.), Třešť 1994, pp. 143–156

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