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

By Ronald R. Yager, Liping Liu

This e-book brings jointly a set of vintage study papers at the Dempster-Shafer idea of trust services. This e-book 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 conscientiously chosen contributions are grouped into seven sections, together with conceptual foundations, theoretical views, theoretical extensions, substitute interpretations, and purposes to man made intelligence, decision-making, and statistical inferences. The booklet additionally contains a foreword through Dempster and Shafer reflecting the improvement of the idea within the final 40 years, and an advent describing the fundamental parts of the idea and the way each one paper contributes to the field.

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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. Cryptographic limitations on learning Boolean formulae and finite automata. Journal of the ACM 41 (1994), 67–95. -A. A Mathematical Theory of Hints. Springer, 1995. [22] Kong, A. Multivariate Belief Functions and Graphical Models.

M(B) Bel(A) = B i x∈Aαi The approach to extending Dempster’s rule was also novel. It considered a multivalued mapping S → T as a compatibility relation S × T and generalized it to a fuzzy relation C : 2S×T → [0, 1], which is a joint possibility distribution. It considered Dempster’s rule as the combination of compatibility relations and generalized it as the combination of fuzzy relations, which in turn is equivalent to the multiplication of noninteractive possibility distributions. 1 Classic Works of the Dempster-Shafer Theory of Belief Functions 25 This led to the generalized rule for combining fuzzy belief functions.

Chapter 8 by Jeffrey Barnett was the first paper dealing with computational issues in implementing Dempster’s rule of combination. , all focal elements are singletons or their negations. Chapter 12 by Jean Gordon and Edward Shortliffe proposed an improved algorithm capable of handling hierarchical evidence, where focal elements and their negations could be arranged in a tree-like structure. To avoid the exponential explosion in computations, the algorithm employed approximation to combine evidence.

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