MAXIMUM ENTROPY ALGORITHMS FOR UNCERTAINTY MEASURES
Y. MAEDA, Hung T. Nguyen, H. Ichihashi · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 1993
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.