Uncertainty in Artificial Intelligence
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Independence Concepts for Convex Sets of Probabilities
Luis de Campos, Serafin Moral
In this paper we study different concepts of independence for convex sets of probabilities. There will be two basic ideas for independence. The first is irrelevance. Two variables are independent when a change on the knowledge about one variable does not affect the other. The second one is factorization. Two variables are independent when the joint convex set of probabilities can be decomposed on the product of marginal convex sets. In the case of the Theory of Probability, these two starting points give rise to the same definition. In the case of convex sets of probabilities, the resulting concepts will be strongly related, but they will not be equivalent. As application of the concept of independence, we shall consider the problem of building a global convex set from marginal convex sets of probabilities.
Keywords: Independence, irrelevance, conditioning, imprecise probabilities, the marginal probl
Pages: 108-115
PS Link: ftp://decsai.ugr.es/pub/utai/other/smc/smc_95_1.ps.Z
PDF Link: /papers/95/p108-de_campos.pdf
AUTHOR = "Luis de Campos and Serafin Moral",
TITLE = "Independence Concepts for Convex Sets of Probabilities",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "108--115"

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