Dynamics of Ising spin chains with nearest-neighbour and next-nearest-neighbour interactions in random fields

2004 ◽  
Vol 241 (15) ◽  
pp. 3607-3623
Author(s):  
G. Ismail ◽  
K. Bannora ◽  
S. Hassan
Keyword(s):  
Random Fields ◽  
Spin Chains ◽  
Nearest Neighbour ◽  
Ising Spin

scholarly journals Ferromagnetic Ising Spin Chains Emerging from the Spin Ice under Magnetic Field

2003 ◽  
Vol 72 (12) ◽  
pp. 3045-3048 ◽  
Author(s):  
Zenji Hiroi ◽  
Kazuyuki Matsuhira ◽  
Masao Ogata
Keyword(s):  
Magnetic Field ◽  
Spin Chains ◽  
Spin Ice ◽  
Ising Spin

scholarly journals Field-induced disorder point in noncollinear Ising spin chains

2004 ◽  
Vol 70 (13) ◽  
Author(s):  
A. Vindigni ◽  
N. Regnault ◽  
Th. Jolicoeur
Keyword(s):  
Spin Chains ◽  
Ising Spin

scholarly journals Direct observation of quantum criticality in Ising spin chains

2009 ◽  
Vol 79 (1) ◽  
Author(s):  
Jingfu Zhang ◽  
Fernando M. Cucchietti ◽  
C. M. Chandrashekar ◽  
Martin Laforest ◽  
Colm A. Ryan ◽  
...  
Keyword(s):  
Direct Observation ◽  
Quantum Criticality ◽  
Spin Chains ◽  
Ising Spin

Generalized Gibbs states and Markov random fields

1973 ◽  
Vol 5 (02) ◽  
pp. 242-261 ◽  
Author(s):  
C. J. Preston
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Finite Graph ◽  
Equilibrium States ◽  
Gibbs States ◽  
Nearest Neighbour ◽  
Markov Random ◽  
Birth Death

It is shown that the set of Markov random fields and Gibbs states with nearest neighbour potentials are the same for any finite graph. The set of Markov random fields is also shown to be the same as the equilibrium states of time-reversible birth/death processes with nearest neighbour interactions defined on the graph.

scholarly journals Julian Ernst Besag. 26 March 1945—6 August 2010

2018 ◽  
Vol 64 ◽  
pp. 27-50
Author(s):  
Peter J. Diggle ◽  
Peter J. Green ◽  
Bernard W. Silverman
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Lattice Models ◽  
Field Trials ◽  
Nearest Neighbour ◽  
Agricultural Field ◽  
Lattice Systems ◽  
Markov Random ◽  
Texture Generation

Julian Besag was an outstanding statistical scientist, distinguished for his pioneering work on the statistical theory and analysis of spatial processes, especially conditional lattice systems. His work has been seminal in statistical developments over the last several decades ranging from image analysis to Markov chain Monte Carlo methods. He clarified the role of auto-logistic and auto-normal models as instances of Markov random fields and paved the way for their use in diverse applications. Later work included investigations into the efficacy of nearest-neighbour models to accommodate spatial dependence in the analysis of data from agricultural field trials, image restoration from noisy data, and texture generation using lattice models.

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