Classic works on the Dempster-Shafer theory of belief by Ronald R. Yager, Liping Liu

By Ronald R. Yager, Liping Liu

This publication brings jointly a suite of vintage learn papers at the Dempster-Shafer concept of trust services. This booklet will function the authoritative reference within the box of evidential reasoning and a big archival reference in a variety of components together with uncertainty reasoning in synthetic intelligence and determination making in economics, engineering, and administration. The rigorously chosen contributions are grouped into seven sections, together with conceptual foundations, theoretical views, theoretical extensions, replacement interpretations, and purposes to man made intelligence, decision-making, and statistical inferences. The booklet additionally encompasses a foreword through Dempster and Shafer reflecting the advance of the speculation within the final 40 years, and an creation describing the fundamental components of the idea and the way each one paper contributes to the field.

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Chapter 15, by Zhang, was one of few contributions that directly improved the classic book by Shafer [34]. Note that the weight of evidence provides a full assessment of evidence for simple and separable support functions. Can a similar concept be extended to support functions that may not be separable? Shafer [34] approached the problem indirectly through notions of internal conflict and impingement. For any separable support function T, let WT and vT be respectively its weight of internal conflict and impingement function.

Wiley, New York, 1964. [17] Fisher, R. A. Inverse probability. Proc. Camb. Phil. Soc. 26 (1930), 154–57, 172–173. Reprinted in Bennett, J. H. (1971). Collected Papers of R. A. Fisher 2, Univ. of Adelaide. [18] Good, I. The measure of a non-measurable set. In Logic, Methodology and Philosophy of Science, E. Nagel, P. Suppes, and A. Tarski, Eds. Stanford University Press, Stanford, 1962, pp. 319–329. [19] Hacking, I. The Emergence of Probability. Cambridge University Press, New York, 1975. , and Valiant, L.

Thus, it does not provide normative axioms or behavioral predictions on how to make decisions and judgments. Because of its expressive power in encoding evidence or modeling uncertainty, however, it has exceptional prescriptive value as a decision support tool. Here we review four chapters demonstrating creative use of belief functions for the purpose, including Chap. 23 by Rajendra P. Srivastava and Glenn Shafer, Chap. 24 by Ronald R. Yager, Chap. 27 by Galina Rogova, and Chap. 29 by Thierry Denoeux.

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