From Statistical Physics to Statistical Inference and Back

Preface. In Place of an Introduction. Concluding Remarks; G. Toulouse. Principles for Inference. Statistical mechanics and the maximum entropy method; R. Balian. Irreversibility, probability and entropy; A.J.M. Garrett. Maximum entropy for random cellular structures; N. Rivier. Minimal Description Length modeling: an introduction; J. Rissanen. An introduction to learning and generalization; G. Parisi. Information geometry and manifolds of neural networks; S.I. Amari. Uncertainty as a resource for managing complexity; G.J. Klir. Coding and Statistical Physics of Disordered Systems. The development of Information Theory; S. Verdu. Statistical inference, zero-knowledge and proofs of identity; J. Stern. Spin glasses: an introduction; M. Mezard. Statistical Mechanics and error-correcting codes; N. Sourlas. Learning. Learning and generalization with undetermined architecture; N. Tishby. Confronting neural network and human behavior in a quasiregular environment; M.A. Virasoro. Sensory processing and information theory; R. Linsker. The formation of representations in the visual cortex; H.-U. Bauer, T. Geisel, K. Pawelzik, F. Wolf. Classifier systems: models for learning agents; D.A. Lane. Dynamical Systems. Space time dynamics and biorthogonal analysis: mementum; R. Lima. Symbolic encoding in dynamical systems; A. Politi. Topological organization of (low-dimensional) chaos; N.B. Tufillaro. Noise separation and MDL modeling of chaotic processes; J. Rissanen. Quantum Mechanics. Inference in Quantum Mechanics; R. Omnes. Decoherence and the existential interpretation of quantum theory or 'no information without representation'; W.H. Zurek. Index.