A Statistical Viewpoint on the Theory of Evidence Classic Reprint
Robert A. Hummel
ISBN 10: 1333644701 ISBN 13: 9781333644703
Editorial: Forgotten Books, 2018
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Descripción
Descripción:
New Book. Shipped from UK. Established seller since 2000. N° de ref. del artículo LW-9781333644703
Sinopsis:
This book offers a novel viewpoint on the Dempster/Shafer Theory of Evidence, a notable framework for reasoning with uncertainty. The author argues that the theory can be understood as a form of statistical analysis of expert opinions. This perspective simplifies the interpretation of the theory's core combination formulas, revealing that they represent Bayesian updating applied to boolean assertions. The book also proposes an alternative formulation that employs probabilistic opinions of experts, leading to simpler formulas and fewer variables while maintaining the essential idea of tracking expert opinion statistics. The combination formula, rather than extending Bayes' theorem for combining probabilities, contains nothing more than Bayes' formula applied to boolean assertions. By treating the formulation as algebraic structures, and by treating opinions as the Statistics on a collection of opinions, the book provides canonical examples with which to construct states of belief to analogous real situations. This approach provides a deeper theoretical basis for the application of canonical examples to probabilistic situations with uncertainties.
Reseña del editor:
Excerpt from A Statistical Viewpoint on the Theory of Evidence
Many problems in artificial intelligence call for assessments of degrees of belief in propositions based on evidence gathered from disparate sources. It is often claimed that probabilistic analysis of propositions is at variance with intuitive notions of belief [7, 17, Various methods have been introduced to reconcile the discrepancies, but no single technique has settled the issue on both theoretical and pragmatic grounds.
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This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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Detalles bibliográficos
Título: A Statistical Viewpoint on the Theory of ...
Editorial: Forgotten Books
Año de publicación: 2018
Encuadernación: PAP
Condición: New
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Paperback. Condición: New. Print on Demand. This book offers a novel viewpoint on the Dempster/Shafer Theory of Evidence, a notable framework for reasoning with uncertainty. The author argues that the theory can be understood as a form of statistical analysis of expert opinions. This perspective simplifies the interpretation of the theory's core combination formulas, revealing that they represent Bayesian updating applied to boolean assertions. The book also proposes an alternative formulation that employs probabilistic opinions of experts, leading to simpler formulas and fewer variables while maintaining the essential idea of tracking expert opinion statistics. The combination formula, rather than extending Bayes' theorem for combining probabilities, contains nothing more than Bayes' formula applied to boolean assertions. By treating the formulation as algebraic structures, and by treating opinions as the Statistics on a collection of opinions, the book provides canonical examples with which to construct states of belief to analogous real situations. This approach provides a deeper theoretical basis for the application of canonical examples to probabilistic situations with uncertainties. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Nº de ref. del artículo: 9781333644703_0
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