Evidence Theory and Its Application: v. 2
Published on 31. July 1992
Book
Hardback
684 pages
978-0-444-89641-4 (ISBN)
Description
An introduction to evidence theory and its applications is presented in this book. It is based on the important Dempster-Shafer theory, which significantly generalizes classic Bayesian statistics and has proved to be useful in a variety of applications. The aim of the volume is to bring the theory up to date by focusing, in particular, on key work by Shafer and Logan as well as on some of the authors' own contributions. The book is intended for a wide range of readers involved in areas as diverse as: artificial intelligence, expert systems, information systems, computer science, decision making, problem solving, business management, statistics, and mathematics. This systematic self-contained description of evidence theory based on set theory is suitable for both lectures and self-study and should serve to strengthen the reader's background and problem-solving abilities.
An introduction to evidence theory and its applications is presented in this book. It is based on the important Dempster-Shafer theory, which significantly generalizes classic Bayesian statistics and has proved to be useful in a variety of applications. The aim of the volume is to bring the theory up to date by focusing, in particular, on key work by Shafer and Logan as well as on some of the authors' own contributions. The book is intended for a wide range of readers involved in areas as diverse as: artificial intelligence, expert systems, information systems, computer science, decision making, problem solving, business management, statistics, and mathematics. This systematic self-contained description of evidence theory based on set theory is suitable for both lectures and self-study and should serve to strengthen the reader's background and problem-solving abilities.
An introduction to evidence theory and its applications is presented in this book. It is based on the important Dempster-Shafer theory, which significantly generalizes classic Bayesian statistics and has proved to be useful in a variety of applications. The aim of the volume is to bring the theory up to date by focusing, in particular, on key work by Shafer and Logan as well as on some of the authors' own contributions. The book is intended for a wide range of readers involved in areas as diverse as: artificial intelligence, expert systems, information systems, computer science, decision making, problem solving, business management, statistics, and mathematics. This systematic self-contained description of evidence theory based on set theory is suitable for both lectures and self-study and should serve to strengthen the reader's background and problem-solving abilities.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Professional and scholarly
Illustrations
Illustrations
Dimensions
Height: 230 mm
ISBN-13
978-0-444-89641-4 (9780444896414)
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Schweitzer Classification
Content
Refining frames of discernment. Partitioning a frame of discernment. The inner and outer coarsening mappings. Coarsening the composition of refinings. Mathematical summary. Support functions. Coarsening the evidential functions. Coarsening mappings and evidential functions. Support mass functions. Mathematical summary. Coarsening operations and mappings. Coarsening operations and orthogonal sum. Relevant interaction of the evidence. Compositions of coarsening operations. Mathematical summary. Refining evidential functions. Refining evidential functions. Coarsenings and evidential functions. Refining operation and the orthogonal sum. Mathematical summary. Refining and coarsening operations. Coarsening refined functions. Refining coarsened functions. Refining and coarsening operations. Mathematical summary. Time linearization for a tree. A review of Barnett's time linearization. Extending from singletons to subsets. The Gordon-Shortliffe tree hierarchy. The Shafer-Logan technique. Mathematical summary. Computations from the bottom up. The first reduction - subroutine 3. The first reduction - subroutine 2. Mathematical summary. Local computation for middle nodes. The second reduction - subroutine 1. The second reduction - subroutine 2. The third reduction - subroutines 1 and 2. The final reduction - subroutine 1. Mathematical summary. Local computations from the bottom. The computation from bottom to top. An example of computation from the bottom. Computations from the top down. Nodes outside the current sub-branch. An equation to solve local computations. Global computation of the orthogonal sum. Framework of the computation from root. Mathematical summary. Computing from the root. The first stage of a round - subroutine 4. The second stage of a round - subroutine 5. The third stage of a round - subroutine 6. Mathematical summary. Finishing the downward computation. The second round - subroutines 4,5 and 6. A summary and the complexity analysis. Completing our example of computation. Mathematical summary. Bibliography. Index.