Articles liés à Relative Information: Theories and Applications
Synopsis
For four decades, information theory has been viewed almost exclusively as a theory based upon the Shannon measure of uncertainty and information, usually referred to as Shannon entropy. Since the publication of Shannon's seminal paper in 1948, the theory has grown extremely rapidly and has been applied with varied success in almost all areas of human endeavor. At this time, the Shannon information theory is a well established and developed body of knowledge. Among its most significant recent contributions have been the use of the complementary principles of minimum and maximum entropy in dealing with a variety of fundamental systems problems such as predic- tive systems modelling, pattern recognition, image reconstruction, and the like. Since its inception in 1948, the Shannon theory has been viewed as a restricted information theory. It has often been argued that the theory is capable of dealing only with syntactic aspects of information, but not with its semantic and pragmatic aspects. This restriction was considered a v~rtue by some experts and a vice by others. More recently, however, various arguments have been made that the theory can be appropriately modified to account for semantic aspects of in- formation as well. Some of the most convincing arguments in this regard are in- cluded in Fred Dretske's Know/edge & Flow of Information (The M.LT. Press, Cambridge, Mass., 1981) and in this book by Guy lumarie.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Présentation de l'éditeur
There are many open problems related to Shannon information theory. For instance, it has long been recognized that the theory does not take account of the subjectivity of the observer, but all previous attempts to deal with this remained at a rather qualitative level. Another problem is the apparent discrepancy between discrete and continuous entropy. And a task of paramount importance is to define the Shannon entropy of a stochastic trajectory and of a deterministic function. This book provides thorough answers to these questions by suitably modifying Shannon theory. It presents a quantitative model of subjective information, a unified approach to discrete and continuous entropy, a theory of information for stochastic functions, and a model of Shannon entropy of deterministic maps which is quite different from Kolmogorov entropy.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.