Information, Physics, and Computation Marc Mezard, Andrea Montanari This book presents a unified approach to a rich and rapidly evolving research domain at the interface between statistical physics, theoretical computer science/discrete mathematics, and coding/information theory. It is accessible to graduate students and researchers without a specific training in any of these fields. The selected topics include spin glasses, error correcting codes, satisfiability, and are central to each field. The approach focuses on large random instances and adopts a common probabilistic formulation in terms of graphical models. It presents massage-passing algorithms such as belief propagation and survey propagation, and their use in decoding and constraint satisfaction solving. It also explains analysis techniques like density evolution and the cavity method, and uses them to study phase transitions. Marc Mezard is a Research Director at CNRS; he works at the Laboratoire de Physique Theorique et Modeles Statistiques, at the Universite Paris Sud., France. Andrea Montanari is a Professor at the Departments of Electrical Engineering and Statistics at Stanford University, USA.