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 ofquantum theory or 'no information without representation' W.H. Zurek. Index.