Information, Physics, and Computation

by Marc Mézard, 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, adopting a common probabilistic formulation in terms of graphical models. It presents message passing algorithms like 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.

Genres: Science, Textbooks, Mathematics, Physics, Computer Science

569 pages, Hardcover

First published January 1, 2009

Reviews

[R Arnaud · Rating 5 out of 5]

Very thorough treatment of modern day statistical physics, and how it is nowadays a set of trick to address problems in information engineering, statistical inference, theoretical computer science and computation in neural systems. What I found particularly nice was the systematic layman translation of each theorem; they always describe why this theorem is important before sketching the proof.

[ShawnLeeZX · Rating 4 out of 5]

Noted to study the idea models in physics.