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I'm looking for a gentle introduction to this topic. The material I've found so far is substantially related to physics, and requires some background in such field.
Is there anything more general and fairly accessible on the web?

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up vote 2 down vote accepted

The tract by Kindermann and Snell called Markov Random Fields and Their Applications, published by the AMS in 1980, is available on the web and a classic (maybe a tad gentler than Brémaud's book indicated by @Learner, which is, in any case, also a must-read on the subject).

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From my own experience, I found chapter 7 in Pierre Brémaud's book "Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues" quite an illuminating introduction to Markov random fields.

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+1 Nice to know. – Tim Dec 14 '12 at 5:43

Have a look at the following papers/book chapters:

  1. S Geman and D Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images".
  2. P Felzenszwalb, R Zabih, "Dynamic Programming and Graph Algorithms in Computer Vision".
  3. D Koller, N Friedman, L Getoor, B Taskar, "Graphical Models in a Nutshell", (book chapter).
  4. C Bishop, "Graphical models" , (book chapter).
  5. K Murphy ,"Undirected graphical models (Markov random fields)", (book chapter).
  6. J Yedidia, W Freeman, Y Weiss, “Understanding Belief Propagation and Its Generalizations".
  7. M. J. Wainwright and M. I. Jordan, "Graphical models, exponential families, and variational inference".
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