Directed Percolation Arising in Stochastic Cellular Automata Analysis
MFCS '08 Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science
Quick Energy Drop in Stochastic 2D Minority
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
On the Analysis of "Simple" 2D Stochastic Cellular Automata
Language and Automata Theory and Applications
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
Theoretical Computer Science
A study on learning robustness using asynchronous 1D cellular automata rules
Natural Computing: an international journal
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Probabilistic cellular automata form a very large and general class of stochastic processes. These automata exhibit a wide range of complex behavior and are of interest in a number of fields of study, including mathematical physics, percolation theory, computer science, and neurobiology. Very little has been proved about these models, even in simple cases, so it is common to compare the models to mean field models. It is normally assumed that mean field models are essentially trivial. However, we show here that even the mean field models can exhibit surprising behavior. We prove some rigorous results on mean field models, including the existence of a surrogate for the “energy” in certain non-reversible models. We also briefly discuss some differences that occur between the mean field and lattice models. © 2006 Wiley Periodicals, Inc. Random Struct. Alg., 2006