Qualitative Versus Quantitative Interpretation of the Mathematical Theory of Evidence

. The paper presents a novel view of the Dempster-Shafer belief function as a measure of diversity in relational data bases. The Dempster rule of evidence combination corresponds to the join operator of the relational database theory. This rough-set based interpretation is qualitative in nature and can represent a number of belief function operators. Keywords: Soft Computing, Knowledge Representation and Integration, Dempster-Shafer theory, rough set theory, relational databases, qualitative interpretation of Dempster rule. 1 Introduction The Dempster-Shafer Theory or the Mathematical Theory of Evidence (MTE) [11, 3] shows one of possible ways of application of mathematical probability for subjective evaluation and is intended to be a generalization of bayesian theory of subjective probability [14, 20]. In spite of its numerous interesting formal properties, many attempts to provide a case-based interpretation of MTE failed [17]. Though a tendency to consider belief functions as subj..

Exploiting sensitivity analysis in Bayesian networks for consumer satisfaction study.

The paper presents an application of Bayesian network technology in a empirical customer satisfaction study. The findings of the study should provide insight as to the importance of product/service dimensions in terms of the strength of their influence on overall satisfaction. To this end we apply a sensitivity analysis of the model’s probabilistic parameters, which enables us to classify the dimensions with respect to their (non) linear and synergy effects on low and high overall satisfaction judgments. Selected results from a real-world case study are shown to demonstrate the usefulness of the approach