# Markov random fields and their applications / Ross Kindermann, J. Laurie Snell

Type de document : MonographieCollection : Contemporary mathematics, 1Langue : anglais.Pays : Etats Unis.Éditeur : Providence : American Mathematical Society, 1980Description : 1 vol. (IX-142 p.) ; 25 cmISBN : 0821850016.ISSN : 0271-4132.Bibliographie : Bibliogr. p. 133-142.Sujet MSC : 60K35, Probability theory and stochastic processes -- Special processes, Interacting random processes; statistical mechanics type models; percolation theory60G60, Probability theory and stochastic processes -- Stochastic processes, Random fields

82B31, Statistical mechanics, structure of matter -- Equilibrium statistical mechanics, Stochastic methods

82B20, Statistical mechanics, structure of matter -- Equilibrium statistical mechanics, Lattice systems (Ising, dimer, Potts, etc.) and systems on graphsEn-ligne : MathSciNet

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CMI Salle R | 60 KIN (Browse shelf) | Available | 10164-01 |

Bibliogr. p. 133-142

This monograph is devoted to a discussion of Markov random fields and the circle of related topics from statistical physics and probability theory. The authors have succeeded in collecting an enormous amount of material from the vast research literature and presenting it at a level accessible to the nonexpert.

The famous Ising model is discussed in Chapter 1. The Peierls contour method is developed and the proof given that the two-dimensional Ising model exhibits a phase transition. Some of the more technical details of this argument are placed in an appendix. A general treatment of Markov random fields and Gibbs measures on finite graphs is presented in Chapter 2. In Chapter 3 the central theme is the limiting behavior, as the graph size tends to infinity, of certain (random) quantities (like the total magnetization in the Ising model). Numerous computer diagrams and simulations are provided which exhibit the qualitative features of these limit theorems. Chapter 4 introduces and discusses briefly a multitude of dynamical systems, including: Russian lamps, voter models, stepping stone model, growth models, percolation models, etc. In Chapter 5 Markov random fields in the Cayley tree are analyzed and Chapter 6 contains some additional applications.

The monograph is an excellent introduction to a fascinating field. (MathScinet)

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