Contextual probability

• Autorzy: Wang H.
• Języki publikacji: EN

Abstrakty

EN

In this paper we present a new probability function G that generalizes the classical probability function. A mass function is an assignment of basic probability to some context (events, propositions). It represents the strength of support for some contexts in a domain. A context is a subset of the basic elements of interest in a domain - the frame of discernment. It is a medium to carry the "probabilistic" knowledge about a domain. The G function is defined in terms of a mass function under various contexts. G is shown to be a probability function satisfying the axioms of probability. Therefore G has all the properties attributed to a probability function. If the mass function is obtained from probability function by normalization, then G is shown to be a linear function of probability distribution and a linear function of probability. With this relationship we can estimate probability distribution from probabilistic knowledge carried in some contexts without any model assumption.

Słowa kluczowe

EN

mathematical foundations; knowledge representation; machine learning; uncertainty; data mining

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2003
• Tom: nr 3
• Strony: 92--97
• Opis fizyczny: Bibliogr. 6 poz., tab.

Twórcy

autor

Wang H.

Bibliografia

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• [2] J. W. Guan and D. A. Bell, “Generalization of the Dempster-Shafer theory” in Proc. IJCAI-93, 1993, pp. 592–597.
• [3] D. Hand, H. Mannila, and P. Smyth, Principles of Data Mining. The MIT Press, 2001.
• [4] E. T. Jaynes, “Probability theory: the logic of science”, http://bayes.wustl.edu
• [5] B. D. Ripley, Pattern Recognition and Neural Networks. Cambridge University Press, 1996.
• [6] G. Shafer, A Mathematical Theory of Evidence. Princeton, New Jersey: Princeton University Press, 1976