A Mathematical Theory of Evidence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Mathematical Theory of Evidence write by Glenn Shafer. This book was released on 2020-06-30. A Mathematical Theory of Evidence available in PDF, EPUB and Kindle. 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.
Combination of Evidence in Dempster-Shafer Theory
Combination of Evidence in Dempster-Shafer Theory - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Combination of Evidence in Dempster-Shafer Theory write by Kari Sentz. This book was released on 2002. Combination of Evidence in Dempster-Shafer Theory available in PDF, EPUB and Kindle. 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.
Classic Works of the Dempster-Shafer Theory of Belief Functions
Classic Works of the Dempster-Shafer Theory of Belief Functions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Classic Works of the Dempster-Shafer Theory of Belief Functions write by Ronald R. Yager. This book was released on 2008-01-22. Classic Works of the Dempster-Shafer Theory of Belief Functions available in PDF, EPUB and Kindle. 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.
Formal Concept Analysis
Formal Concept Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Formal Concept Analysis write by Bernhard Ganter. This book was released on 2012-12-06. Formal Concept Analysis available in PDF, EPUB and Kindle. This first textbook on formal concept analysis gives a systematic presentation of the mathematical foundations and their relations to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. The mathematical foundations are treated thoroughly and are illuminated by means of numerous examples, making the basic theory readily accessible in compact form.
Belief Functions: Theory and Applications
Belief Functions: Theory and Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Belief Functions: Theory and Applications write by Thierry Denoeux. This book was released on 2012-04-26. Belief Functions: Theory and Applications available in PDF, EPUB and Kindle. The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories. This volume contains the proceedings of the 2nd International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles.
