In decision-making problems when probabilistic information is incomplete (e.g. Nguyen and Walker [1]) as well as in measuring the non-specificity and conflict in the theory of evidence (e.g. Maeda and Ichihashi [2, 3]), one is led to consider the problem of maximizing probabilistic entropy under a functional constraint induced by the available evidence. This paper is devoted specifically to developing computational algorithms for this optimization problem.
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Maeda et al. (1993) studied this question.
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