Generalized combination rule for evidential reasoning approach and Dempster– Shafer theory of evidence

Generalized combination rule for evidential reasoning approach and Dempster– Shafer theory of evidence PDF Author: Yuan-Wei Du
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 40

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Book Description
The Dempster–Shafer (DS) theory of evidence can combine evidence with one parameter. The evidential reasoning (ER) approach is an extension of DS theory that can combine evidence with two parameters (weights and reliabilities). However, it has three infeasible aspects: reliability dependence, unreliability effectiveness, and intergeneration inconsistency.

Generalized combination rule for evidential reasoning approach and Dempster– Shafer theory of evidence

Generalized combination rule for evidential reasoning approach and Dempster– Shafer theory of evidence PDF Author: Yuan-Wei Du
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 40

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Book Description
The Dempster–Shafer (DS) theory of evidence can combine evidence with one parameter. The evidential reasoning (ER) approach is an extension of DS theory that can combine evidence with two parameters (weights and reliabilities). However, it has three infeasible aspects: reliability dependence, unreliability effectiveness, and intergeneration inconsistency.

A Mathematical Theory of Evidence

A Mathematical Theory of Evidence PDF Author: Glenn Shafer
Publisher: Princeton University Press
ISBN: 0691214697
Category : Mathematics
Languages : en
Pages :

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Book Description
Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combination, this book constructs a new theory of epistemic probability. The theory draws on the work of A. P. Dempster but diverges from Depster's viewpoint by identifying his "lower probabilities" as epistemic probabilities and taking his rule for combining "upper and lower probabilities" as fundamental. The book opens with a critique of the well-known Bayesian theory of epistemic probability. It then proceeds to develop an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions. This rule, together with the idea of "weights of evidence," leads to both an extensive new theory and a better understanding of the Bayesian theory. The book concludes with a brief treatment of statistical inference and a discussion of the limitations of epistemic probability. Appendices contain mathematical proofs, which are relatively elementary and seldom depend on mathematics more advanced that the binomial theorem.

Classic Works of the Dempster-Shafer Theory of Belief Functions

Classic Works of the Dempster-Shafer Theory of Belief Functions PDF Author: Ronald R. Yager
Publisher: Springer
ISBN: 354044792X
Category : Technology & Engineering
Languages : en
Pages : 813

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Book Description
This is a collection of classic research papers on the Dempster-Shafer theory of belief functions. The book is the authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management. The book includes a foreword reflecting the development of the theory in the last forty years.

Combination of Evidence in Dempster-Shafer Theory

Combination of Evidence in Dempster-Shafer Theory PDF Author: Kari Sentz
Publisher:
ISBN:
Category : Dempster-Shafer theory
Languages : en
Pages : 100

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Book Description
Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.

On the belief universal gravitation (BUG)

On the belief universal gravitation (BUG) PDF Author: Xiangjun Mi
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 30

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Book Description
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Propositional, Probabilistic and Evidential Reasoning

Propositional, Probabilistic and Evidential Reasoning PDF Author: Weiru Liu
Publisher: Physica
ISBN: 3790818119
Category : Computers
Languages : en
Pages : 279

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Book Description
How to draw plausible conclusions from uncertain and conflicting sources of evidence is one of the major intellectual challenges of Artificial Intelligence. It is a prerequisite of the smart technology needed to help humans cope with the information explosion of the modern world. In addition, computational modelling of uncertain reasoning is a key to understanding human rationality. Previous computational accounts of uncertain reasoning have fallen into two camps: purely symbolic and numeric. This book represents a major advance by presenting a unifying framework which unites these opposing camps. The Incidence Calculus can be viewed as both a symbolic and a numeric mechanism. Numeric values are assigned indirectly to evidence via the possible worlds in which that evidence is true. This facilitates purely symbolic reasoning using the possible worlds and numeric reasoning via the probabilities of those possible worlds. Moreover, the indirect assignment solves some difficult technical problems, like the combinat ion of dependent sources of evidcence, which had defeated earlier mechanisms. Weiru Liu generalises the Incidence Calculus and then compares it to a succes sion of earlier computational mechanisms for uncertain reasoning: Dempster-Shafer Theory, Assumption-Based Truth Maintenance, Probabilis tic Logic, Rough Sets, etc. She shows how each of them is represented and interpreted in Incidence Calculus. The consequence is a unified mechanism which includes both symbolic and numeric mechanisms as special cases. It provides a bridge between symbolic and numeric approaches, retaining the advantages of both and overcoming some of their disadvantages.

Classic Works of the Dempster-Shafer Theory of Belief Functions

Classic Works of the Dempster-Shafer Theory of Belief Functions PDF Author: Ronald R. Yager
Publisher: Springer Science & Business Media
ISBN: 3540253815
Category : Mathematics
Languages : en
Pages : 813

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Book Description
This is a collection of classic research papers on the Dempster-Shafer theory of belief functions. The book is the authoritative reference in the field of evidential reasoning and an important archival reference in a wide range of areas including uncertainty reasoning in artificial intelligence and decision making in economics, engineering, and management. The book includes a foreword reflecting the development of the theory in the last forty years.

A new weighting factor in combining belief function

A new weighting factor in combining belief function PDF Author: Deyun Zhou
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 20

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Book Description
Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences.

Uncertainty-Based Information

Uncertainty-Based Information PDF Author: George Klir
Publisher: Springer Science & Business Media
ISBN: 9783790812428
Category : Mathematics
Languages : en
Pages : 196

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Book Description
Information is precious. It reduces our uncertainty in making decisions. Knowledge about the outcome of an uncertain event gives the possessor an advantage. It changes the course of lives, nations, and history itself. Information is the food of Maxwell's demon. His power comes from know ing which particles are hot and which particles are cold. His existence was paradoxical to classical physics and only the realization that information too was a source of power led to his taming. Information has recently become a commodity, traded and sold like or ange juice or hog bellies. Colleges give degrees in information science and information management. Technology of the computer age has provided access to information in overwhelming quantity. Information has become something worth studying in its own right. The purpose of this volume is to introduce key developments and results in the area of generalized information theory, a theory that deals with uncertainty-based information within mathematical frameworks that are broader than classical set theory and probability theory. The volume is organized as follows.

Multisensor Data Fusion

Multisensor Data Fusion PDF Author: David Hall
Publisher: CRC Press
ISBN: 1420038540
Category : Technology & Engineering
Languages : en
Pages : 564

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Book Description
The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut