An intelligent quality-based approach to fusing multi-source probabilistic information

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 aggregations of source provided probability distributions. © 2016 Elsevier B.V. All rights reserved.