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MAXIMUM ENTROPY ALGORITHMS FOR UNCERTAINTY MEASURES
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1993
Year
Mathematical ProgrammingEngineeringComputational ComplexityProbabilistic ComputationUncertain DataUncertainty FormalismUncertainty ModelingProbabilistic OntologyData ScienceUncertainty QuantificationComputational AlgorithmsProbabilistic ReasoningManagementCombinatorial OptimizationDecision TheoryAvailable EvidenceProbability TheoryComputer ScienceAlgorithmic Information TheoryEntropyImprecise ProbabilityFunctional Constraint
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.