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OverviewPhysicists, when modelling physical systems with a large number of degrees of freedom, and statisticians, when performing data analysis, have developed their own concepts and methods for making the ""best"" inference. But are these methods equivalent, or not? What is the state of the art in making inferences? Moreover, neural computation demands a clearer understanding of how neural systems make inferences; the theory of chaotic nonlinear systems as applied to time series analysis could profit from the experience already booked by the statisticians; and finally, there is a long-standing conjecture that some of the puzzles of quantum mechanics are due to our incomplete understanding of how we make inferences. Such questions are considered in this text. But other considerations also arise, such as the maximum entropy method and Bayesian inference, information theory and the minimum description length. Finally, it is pointed out that an understanding of human inference may require input from psychologists. This debate is summarized in the present work. Full Product DetailsAuthor: P. Grassberger , J.P. NadalPublisher: Springer Imprint: Springer Edition: 1994 ed. Volume: 428 Dimensions: Width: 15.50cm , Height: 2.00cm , Length: 23.50cm Weight: 1.530kg ISBN: 9780792327752ISBN 10: 0792327756 Pages: 355 Publication Date: 31 March 1994 Audience: College/higher education , Professional and scholarly , Postgraduate, Research & Scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsSome remarks on.- Statistical mechanics and the maximum entropy method.- Irreversibility, probability and entropy.- Maximum entropy for random cellular structures.- Minimal Description Length modeling: an introduction.- An introduction to learning and generalization.- Information geometry and manifolds- of neural networks.- Uncertainty as a resource for managing complexity.- The development of Information Theory.- Statistical inference, zero-knowledge and proofs of identity.- Spin glasses: an introduction.- Statistical Mechanics and error-correcting codes.- Learning and generalization with undetermined architecture.- Confronting neural network and human behavior in a quasiregular environment.- Sensory processing and information theory.- The formation of representations in the visual cortex.- Classifier systems: models for learning agents.- Space time dynamics and biorthogonal analysis: mementum.- Symbolic encoding in dynamical systems.- Topological organization of (low-dimensional) chaos.- Noise Separation and MDL modeling of chaotic processes.- Inference in Quantum Mechanics.- Decoherence and the existential interpretation of quantum theory or “no information without representation”.- List of Contributors.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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