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Information, Physics, and Computation$
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Marc Mézard and Andrea Montanari

Print publication date: 2009

Print ISBN-13: 9780198570837

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780198570837.001.0001

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Bridges: Inference and the Monte Carlo method

Bridges: Inference and the Monte Carlo method

Chapter:
(p.267) 13 Bridges: Inference and the Monte Carlo method
Source:
Information, Physics, and Computation
Author(s):

Marc Mézard

Andrea Montanari

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198570837.003.0013

The mathematical structure highlighted in this chapter by the factor graph representation is the locality of probabilistic dependencies between variables. Locality also emerges in many problems of probabilistic inference, which provides another unifying view of the field. This chapter describes coding theory, statistical physics, and combinatorial optimization as inference problems. It also explores one generic inference method, the use of Monte Carlo Markov chains (MCMC) in order to sample from complex probabilistic models. Many of the difficulties encountered in decoding, in constraint satisfaction problems, or in glassy phases, are connected to a dramatic slowing down of MCMC dynamics, which is explored through simple numerical experiments on some examples.

Keywords:   inference, Monte Carlo, Markov chain, MCMC dynamics, coding theory, combinatorial optimization

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