Abstract
Probability intervals constitute an interesting formalism for representing uncertainty. In order to use them together with belief networks, we study basic concepts as marginalization, conditioning and independence for probability intervals. Then we develop some algorithms for learning simple belief networks (trees and polytrees), based on this kind of non purely probabilistic information.
This work has been supported by the European Economic Community under Project Esprit III b.r.a. 6156 (DRUMS II)
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References
S. Acid, L.M. de Campos, A. González, R. Molina, N. Pérez de la Blanca: Learning with CASTLE, in Symbolic and Quantitative Approaches to Uncertainty, Lecture Notes in Computer Science 548, R. Kruse, P. Siegel (Eds.), Springer Verlag (1991) 99–106
L.M. de Campos, M.T. Lamata, S. Moral: Logical connectives for combining fuzzy measures, in Methodologies for Intelligent Systems 3, Z.W. Ras, L. Saitta (Eds.), North-Holland, New York (1988) 11–18
L.M. de Campos, M.T. Lamata, S. Moral: The concept of conditional fuzzy measure, International Journal of Intelligent Systems 5 (1990) 237–246
J.E. Cano, S. Moral, J.F. Verdegay: Propagation of convex sets of probabilities in directed acyclic networks, Proceedings of the Fourth IPMU Conference (1992) 289–292
C.K. Chow, C.N. Liu: Approximating discrete probability distribution with dependence trees, IEEE Transactions on Information Theory 14 (1968) 462–467
G.F. Cooper, E. Herskovits: A Bayesian Method for the Induction of Probabilistic Networks from Data, Machine Learning 9 (1992) 309–347
H.E. Kyburg: Bayesian and non-bayesian evidential updating, Artificial Intelligence 31 (1987) 271–293
S.L. Lauritzen, D.J. Spiegelhalter: Local Computations with probabilities on graphical structures and their applications to expert systems, Journal of the Royal Statistical Society B-50 (1988) 157–224
J. Pearl: Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan and Kaufmann, San Mateo (1988)
G. Rebane, J. Pearl: The recovery of causal poly-trees from statistical data, in Uncertainty in Artificial Intelligence 3, L.N. Kanal, T.S. Levitt and J.F. Lemmer (Eds.), North-Holland (1989) 175–182
G. Shafer, P.P. Shenoy: Axioms for probability and belief function propagation, in Uncertainty in Artificial Intelligence 4, R.D. Shachter, T.S. Levitt, L.N. Kanal, J.F. Lemmer (Eds.), North-Holland, Amsterdam (1990) 169–198
P. Spirtes: Detecting causal relations in the presence of unmeasured variables, Uncertainty in Artificial Intelligence, Proc. of the Seventh Conference (1991) 392–397
B. Tessem: Interval representation on uncertainty in Artificial Intelligence, Ph. D. Thesis, Department of Informatics, University of Bergen, Norway (1989)
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© 1993 Springer-Verlag Berlin Heidelberg
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de Campos, L.M., Huete, J.F. (1993). Learning non probabilistic belief networks. In: Clarke, M., Kruse, R., Moral, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1993. Lecture Notes in Computer Science, vol 747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028182
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DOI: https://doi.org/10.1007/BFb0028182
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