By Ronald R. Yager, Liping Liu
This publication brings jointly a suite of vintage learn papers at the Dempster-Shafer idea 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 choice making in economics, engineering, and administration. The rigorously chosen contributions are grouped into seven sections, together with conceptual foundations, theoretical views, theoretical extensions, substitute interpretations, and functions to man made intelligence, decision-making, and statistical inferences. The publication additionally contains a foreword via Dempster and Shafer reflecting the advance of the speculation within the final 40 years, and an advent describing the elemental components of the idea and the way each one paper contributes to the field.
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Additional resources for Classic works on the Dempster-Shafer theory of belief functions: 43 tables
The chapter oﬀered some alternative designs for using the languages of Bayesian probabilities and belief functions. For example, the total-evidence design often used with Bayesian theory is distinguished from the belief function that emphasizes the decomposition of evidence. The chapter emphasized that theories of subjective probability (including belief functions) were not psychological models, either normative or descriptive, for making judgments. An experimental design for using such a theory (or its semantics and syntax) must guide the process of making probabilistic judgments.
4 derived upper and lower probabilities for the same parameters, it did so without explicitly invoking the rule of combination. Chap. 4 framed the inference problem using a formal model (S, m, Γ, T ), where S is a population, m is a probability measure governing how each individual may be sampled from the population, and T = X × Θ is the product of the set of all possible observations x and the set of all possible parameter values θ. A multivalued mapping Γ : S → T was then used to derive a restricted mapping Γθ : S → X when the parameter θ is ﬁxed or Γx : S → Θ when an observation x is made.
Liu and R. R. Yager characterized probability as a degree of certainty that diﬀers from absolute certainty as a part diﬀers from a whole. The art of conjecture was to measure as exactly as possible the probabilities of things. With respect to games of chance, the symmetry of physical devices suggested we could calculate the probability of a speciﬁed outcome as the number of favorable cases divided by the total number of cases. In many other situations, however, such symmetry could not be relied upon and the classical procedure could not be applied.
Classic works on the Dempster-Shafer theory of belief functions: 43 tables by Ronald R. Yager, Liping Liu