Uncertainty in Artificial Intelligence
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Conditional Independence and Markov Properties in Possibility Theory
Jirina Vejnarova
Abstract:
Conditional independence and Markov properties are powerful tools allowing expression of multidimensional probability distributions by means of low-dimensional ones. As multidimensional possibilistic models have been studied for several years, the demand for analogous tools in possibility theory seems to be quite natural. This paper is intended to be a promotion of de Cooman's measure-theoretic approcah to possibility theory, as this approach allows us to find analogies to many important results obtained in probabilistic framework. First, we recall semi-graphoid properties of conditional possibilistic independence, parameterized by a continuous t-norm, and find sufficient conditions for a class of Archimedean t-norms to have the graphoid property. Then we introduce Markov properties and factorization of possibility distrubtions (again parameterized by a continuous t-norm) and find the relationships between them. These results are accompanied by a number of conterexamples, which show that the assumptions of specific theorems are substantial.
Keywords:
Pages: 609-616
PS Link:
PDF Link: /papers/00/p609-vejnarova.pdf
BibTex:
@INPROCEEDINGS{Vejnarova00,
AUTHOR = "Jirina Vejnarova ",
TITLE = "Conditional Independence and Markov Properties in Possibility Theory",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "609--616"
}


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