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Dempster, A.P. (1967) Upper and Lower Probabilities Induced by a Multi-valued Mapping. Ann. Math, Statist. 38, 325–339.
Gordon, J. and Shortliffe, E.H. (1985) A Method of Managing Evidential Reasoning in a Hierarchical Hypothesis Space, Artificial Intelligence, 26, 323–357.
Kampke (1988) About Assessing and Evaluating Uncertain Inferences Within the Theory of Evidence. Decision Support Systems 4, 433–439.
Kreinovich, V. and Borrett, W. (1990) Monte-Carlo Methods Allow to Avoid Exponential Time in Dempster-Shafer Formalism. Tech.Report UTEP-CS-90-5, Computer Science Dept. University of Texas at El Paso.
Kyburg, H.E., Jr. (1987) Bayesian and Non-Bayesian Evidential Updating. Artificial Intelligence 31, 271–293.
Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
Shafer, G. and Logan, R. (1987) Implementing Dempster's Rule for Hierarchical Evidence. Artificial Intelligence, 33, 271–298.
Shafer and Shenoy, P.P. (1988) Local Computation in Hypertrees, Working Paper No. 201, School of Business, University of Kansas.
Kennes, R. and Smets, P. (1990) Computational Aspects of the Mobius Transform. Proc. 6 th Conference on Uncertainty in Artificial Intelligence, Cambridge, Mass.
Wasserman, L.A. (1990) Prior Envelopes Based on Belief Functions. Annals of Statistics, 18, 1, 454–464.
Wilson, P.N. (1989) Justification, Computational Efficiency and Generalisation of the Dempster-Shafer Theory. Research Report No. 15, Dept. of Computing and Math. Sciences, Oxford Polytechnic. Also to appear in Artificial Intelligence.
Wilson, P.N. (1991a) A Monte-Carlo Algorithm for Dempster-Shafer Belief, Research Report, Dept. of Computer Science, Queen Mary and Westfield College. Also to appear in Proceedings of 7 th Conference on Uncertainty in Artificial Intelligence.
Wilson, P.N. (1991b) Likelihood Updating of a Belief Function, Research Report, Dept. of Computer Science, Queen Mary and Westfield College, University of London.
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Clarke, M., Wilson, N. (1991). Efficient algorithms for belief functions based on the relationship between belief and probability. In: Kruse, R., Siegel, P. (eds) Symbolic and Quantitative Approaches to Uncertainty. ECSQARU 1991. Lecture Notes in Computer Science, vol 548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54659-6_64
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DOI: https://doi.org/10.1007/3-540-54659-6_64
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