Formal Theories of Information: From Shannon to Semantic Information Theory and General Concepts of Information (Lecture Notes in Computer Science, 5363) 2009th Edition by Giovanni Sommaruga (PDF)

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It is commonly assumed that computers process information. But what is inf- mation? In a technical, important, but nevertheless rather narrow sense, Sh- non’sinformationtheorygivesa?rstanswertothisquestion.Thistheoryfocuses on measuring the information content of a message. Essentially this measure is the reduction of the uncertainty obtained by receiving a message. The unc- tainty of a situation of ignorance in turn is measured by entropy. This theory hashad an immense impact on the technologyof information storage,data c- pression, information transmission and coding and still is a very active domain of research. Shannon’s theory has also attractedmuch interest in a more philosophic look at information, although it was readily remarked that it is only a “syntactic” theory of information and neglects “semantic” issues. Several attempts have been made in philosophy to give information theory a semantic ?avor, but still mostly based on or at least linked to Shannon’s theory. Approaches to semantic informationtheoryalsoveryoftenmakeuseofformallogic.Thereby,information is linked to reasoning, deduction and inference, as well as to decision making. Further, entropy and related measure were soon found to have important connotations with regard to statistical inference. Surely, statistical data and observation represent information, information about unknown, hidden para- ters. Thus a whole branch of statistics developed around concepts of Shannon’s information theory or derived from them. Also some proper measurements – propriate for statistics, like Fisher’s information, were proposed.

User’s Reviews

Editorial Reviews: Review From the reviews:“This new anthology on formal theories of information is based upon research presented at the May 2006 Muenchenwiler seminar of the Information and Knowledge research groups of the computer science departments of the universities of Bern, Fribourg, and Neuchatel. … This is probably the clearest account of algorithmic information theory that one will come across. … Formal theories of information and their philosophical analysis are being developed right now, and this is what makes a volume of this quality so welcome.” (Sebastian Sequoiah-Grayson, Minds and Machines, Vol. 22, 2012) From the Back Cover This book presents the scientific outcome of a joint effort of the computer science departments of the universities of Berne, Fribourg and Neuchâtel.Within an initiative devoted to “Information and Knowledge”, these research groups collaborated over several years on issues of logic, probability, inference, and deduction. The goal of this volume is to examine whether there is any common ground between the different approaches to the concept of information.The structure of this book could be represented by a circular model, with an innermost syntactical circle, comprising statistical and algorithmic approaches; a second, larger circle, the semantical one, in which “meaning” enters the stage; and finally an outermost circle, the pragmatic one, casting light on real-life logical reasoning.These articles are complemented by two philosophical contributions exploring the wide conceptual field as well as taking stock of the articles on the various formal theories of information.

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