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This monograph builds on the foundational work of Arthur Dempster, Glenn Shafer, and R. A. Fisher in statistical inference, focusing on the concept of statistical information. It introduces a novel inferential mechanism termed assumption-based reasoning, which blends logic and classical probability theory. While traces of this idea appear in Jacob Bernoulli's writings, the book offers a comprehensive description, showcasing that assumption-based inference on functional models generalizes both Bayesian and Fisher's fiducial inference, addressing the longstanding debate between these theories. The approach clarifies post-data probabilistic statements about unknown parameters, revealing that the likelihood function does not encompass all statistical information from an experiment. The book describes statistical information through functional models, illustrating how observations relate to unknown parameters and stochastic disturbances. The first part examines discrete functional models, presenting assumption-based reasoning without technical complexities, demonstrating how to combine information to focus on specific questions. It introduces an algebraic perspective on statistical information analysis and preliminary results on hypothesis selection. The second part explores continuous models through assumption-based reasoning, clarifying concepts in Fisher's fiducial theory and the role of improper priors in Bayesian inference. Th

Modern civilization relies on a functioning information infrastructure. As a result, dependability has become a central issue in all disciplines of systems engineering and software architecture. Theories, methods and tools that help to master the problems encountered in the design process and the management of operations are therefore of utmost importance for the future of information and communication technology. The present volume documents the results of a research program on Dependable Information and Communication Systems (DICS). The members of the project met in two workshops organized by the Hasler Foundation. This state-of-the-art survey contains 3 overview articles identifying major issues of dependability and presenting the latest solutions, as well as 10 carefully selected and revised papers depicting the research results originating from those workshops. The first workshop took place in Münchenwiler, Switzerland, in March 2004, and the second workshop, which marked the conclusion of the projects, in Löwenberg, Switzerland, in October 2005. The papers are organized in topical sections on surveys, dependable software, dependable computing, and dependable networks.

Information usually comes in pieces, from different sources. It refers to different, but related questions. Therefore information needs to be aggregated and focused onto the relevant questions. Considering combination and focusing of information as the relevant operations leads to a generic algebraic structure for information. This book introduces and studies information from this algebraic point of view. Algebras of information provide the necessary abstract framework for generic inference procedures. They allow the application of these procedures to a large variety of different formalisms for representing information. At the same time they permit a generic study of conditional independence, a property considered as fundamental for knowledge presentation. Information algebras provide a natural framework to define and study uncertain information. Uncertain information is represented by random variables that naturally form information algebras. This theory also relates to probabilistic assumption-based reasoning in information systems and is the basis for the belief functions in the Dempster-Shafer theory of evidence.