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Upper and lower entropies of belief functions using compatible probability functions

  • Approximate Reasoning
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Methodologies for Intelligent Systems (ISMIS 1993)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 689))

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Abstract

This paper uses the compatible probability functions to define the notion of upper entropy and lower entropy of a belief function as a generalization of the Shannon entropy. The upper entropy measures the amount of information conveyed by the evidence currently available. The lower entropy measures the maximum possible amount of information that can be obtained if further evidence becomes available. This paper also analyzes the different characteristics of these entropies and the computational aspect. The study demonstrates usefulness of compatible probability functions to apply various notions from the probability theory to the theory of belief functions.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    C. W. R. Chau, P. Lingras & S. K. M. Wong

Authors
  1. C. W. R. Chau
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  2. P. Lingras
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  3. S. K. M. Wong
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Editor information

Jan Komorowski Zbigniew W. Raś

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© 1993 Springer-Verlag Berlin Heidelberg

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Chau, C.W.R., Lingras, P., Wong, S.K.M. (1993). Upper and lower entropies of belief functions using compatible probability functions. In: Komorowski, J., Raś, Z.W. (eds) Methodologies for Intelligent Systems. ISMIS 1993. Lecture Notes in Computer Science, vol 689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56804-2_29

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  • DOI: https://doi.org/10.1007/3-540-56804-2_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56804-9

  • Online ISBN: 978-3-540-47750-1

  • eBook Packages: Springer Book Archive

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