Generalized information theory for hints

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• 作者：Marc Pouly^a ; ^c ; ^{marc.pouly@hslu.ch} ; Juerg Kohlas^b ; ^{juerg.kohlas@unifr.ch} ; Peter Y A Ryan^c ; ^{peter.ryan@uni.lu}
• 关键词：Information theory ; Theory of Hints ; Dempster-Shafer theory
• 刊名：International Journal of Approximate Reasoning
• 出版年：2013
• 出版时间：January, 2013
• 年：2013
• 卷：54
• 期：1
• 页码：228-251
• 全文大小：475 K

文摘

This paper develops a new uncertainty measure for the theory of hints that complies with the established semantics of statistical information theory and further satisfies all classical requirements for such a measure imposed in the literature. The proposed functional decomposes into conversant uncertainty measures and therefore discloses a new interpretation of the latters as well. By abstracting to equivalence classes of hints we transport the new measure to mass functions in Dempster-Shafer theory and analyse its relationship with the aggregate uncertainty, which currently is the only known functional for the Dempster-Shafer theory of evidence that satisfies the same set of properties. Moreover, the perspective of hints reveals that the standard independence notion in Dempster-Shafer theory called non-interactivity corresponds to an amalgamation of probabilistic independence and qualitative independence between frames of discernment. All results in this paper are developed for arbitrary families of compatible frames generalizing the very specialized multi-variate systems that are usually studied in information theory.