One-dimensional Ising model in a variety of random fields

1986 ◽  
Vol 34 (9) ◽  
pp. 6214-6218 ◽  
Author(s):  
David Andelman
Keyword(s):  
Ising Model ◽  
Random Fields ◽  
One Dimensional

One-dimensional kinetic ising model with one impurity

1985 ◽  
Vol 58 (4) ◽  
pp. 319-322 ◽  
Author(s):  
N. T. de Oliveira ◽  
L. L. Gonçalves ◽  
F. C. Sá Barreto
Keyword(s):  
Ising Model ◽  
Kinetic Ising Model ◽  
One Dimensional

scholarly journals Autoregressive Models of the Random fields—A Survey

2020 ◽  
pp. 1-9
Keyword(s):  
Random Fields ◽  
Linear Models ◽  
Estimation Methods ◽  
Suitable Model ◽  
Main Mode ◽  
One Dimensional ◽  
Non Linear ◽  
The Status ◽  
Ar Models ◽  
Shed Light

Autoregressive (AR) random fields are widely use to describe changes in the status of real-physical objects and implemented for analyzing linear & non-linear models. AR models are Markov processes with a higher order dependence for one-dimensional time series. Actually, various estimation methods were used in order to evaluate the autoregression parameters. Although in many applications background knowledge can often shed light on the search for a suitable model, but other applications lack this knowledge and often require the type of trial errors to choose a model. This article presents a brief survey of the literatures related to the linear and non-linear autoregression models, including several extensions of the main mode models and the models developed. The use of autoregression to describe such system requires that they be of sufficiently high orders which leads to increase the computational costs.

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