Abstract
The MinMax measure of information, defined by Kapur, Baciu and Kesavan [6], is a quantitative measure of the information contained in a given set of moment constraints. It is based on both maximum and minimum entropy. Computational difficulties in the determination of minimum entropy probability distributions (MinEPD) have inhibited exploration of the full potential of minimum entropy and, hence, the MinMax measure. Initial attempts to solve the minimum entropy problem were directed towards finding analytical solutions for some specific set of constraints. Here, we present a numerical solution to the general minimum entropy problem and discuss the significance of minimum entropy and the MinMax measure. Some numerical examples are given for illustration.
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Srikanth, M., Kesavan, H.K., Roe, P. (2003). Computation of the MinMax Measure. In: Karmeshu (eds) Entropy Measures, Maximum Entropy Principle and Emerging Applications. Studies in Fuzziness and Soft Computing, vol 119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36212-8_13
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DOI: https://doi.org/10.1007/978-3-540-36212-8_13
Publisher Name: Springer, Berlin, Heidelberg
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