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Erschienen in:
2019 | OriginalPaper | Buchkapitel
3. Using Quality Measures in the Intelligent Fusion of Probabilistic Information
verfasst von : Ronald R. Yager, Frederick E. Petry
Erschienen in: Information Quality in Information Fusion and Decision Making
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Abstract
Our objective here is to obtain quality-fused values from multiple sources of probabilistic distributions, where quality is related to the lack of uncertainty in the fused value and the use of credible sources. We first introduce a vector representation for a probability distribution. With the aid of the Gini formulation of entropy, we show how the norm of the vector provides a measure of the certainty, i.e., information, associated with a probability distribution. We look at two special cases of fusion for source inputs, those that are maximally uncertain and certain. We provide a measure of credibility associated with subsets of sources. We look at the issue of finding the highest-quality fused value from the weighted aggregation of source-provided probability distributions.