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. 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.

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.

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.

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.

A self-contained examination of all aspects of statistical evidence evaluation in forensic science, from theory to concrete applications.

Koslowski boldly criticizes many of the currently classic studies and musters a compelling set of arguments, backed by an exhaustive set of experiments carried out during the last decade.

My interest in X-Efficiency (XE) dates back to 1978. At the time, I was writing the dissertation for my Ph. D. at Washington State University. My dissertation was concerned with the role of attitudes in the school-to-work transition among young men. I was advised by Professor Millard Hastay (a member of my committee) to look at Leibenstein's "new" book, Beyond Economic Man. One of the things that caught my attention was his be havioral description of (selective) rationality. It seemed that Leibenstein's behavioral description of a (selectively) rational individual was very similar to what psychologists such as Abraham Maslow were reporting as being the product of a particular motivational system. In other words, I was im pressed with the idea that what Leibenstein was referring to as X-ineffi ciency was being discussed by psychologists as "the way it (often) is. " So from the beginning I always considered the concept of X-(in)efficiency to be a valuable one for understanding human behavior. I have since come to believe that this is particularly true when considering behavior in non market environments, i. e. , within the firm. Work on this book, however, can most realistically said to have started with work which I began in 1982 while I was a Visiting Scholar at Harvard University. Professor Leibenstein suggested that I consider how some em pirical evidence which was being cited as evidence for the role of property rights might also be consistent with XE theory.