Keywords
Complexity
Entropy
How to Cite
Abstract
The complete framework for the minimal deterministic automata construction of the one-dimensional Ising model is presented. The approach follows the known treatment of the Ising model as a Markov random field, where the local characteristic is usually obtained from the stochastic matrix. The problem is the inverse relation or how to get the stochastic matrix from the local characteristics given via the transfer matrix treatment. The obtained expressions allow for performing complexity-entropy analysis of particular instances of the Ising model. Two examples are discussed: the 1/2-spin nearest neighbour and next nearest neighbours Ising model.
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