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Predictability in Cellular Automata

Modelled as finite homogeneous Markov chains, probabilistic cellular automata with local transition probabilities in (0, 1) always posses a stationary distribution. This result alone is not very helpful when it comes to predicting the final configuration; one needs also a formula connecting the prob...

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Detalles Bibliográficos
Autores principales: Agapie, Alexandru, Andreica, Anca, Chira, Camelia, Giuclea, Marius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182702/
https://www.ncbi.nlm.nih.gov/pubmed/25271778
http://dx.doi.org/10.1371/journal.pone.0108177
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author Agapie, Alexandru
Andreica, Anca
Chira, Camelia
Giuclea, Marius
author_facet Agapie, Alexandru
Andreica, Anca
Chira, Camelia
Giuclea, Marius
author_sort Agapie, Alexandru
collection PubMed
description Modelled as finite homogeneous Markov chains, probabilistic cellular automata with local transition probabilities in (0, 1) always posses a stationary distribution. This result alone is not very helpful when it comes to predicting the final configuration; one needs also a formula connecting the probabilities in the stationary distribution to some intrinsic feature of the lattice configuration. Previous results on the asynchronous cellular automata have showed that such feature really exists. It is the number of zero-one borders within the automaton's binary configuration. An exponential formula in the number of zero-one borders has been proved for the 1-D, 2-D and 3-D asynchronous automata with neighborhood three, five and seven, respectively. We perform computer experiments on a synchronous cellular automaton to check whether the empirical distribution obeys also that theoretical formula. The numerical results indicate a perfect fit for neighbourhood three and five, which opens the way for a rigorous proof of the formula in this new, synchronous case.
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spelling pubmed-41827022014-10-07 Predictability in Cellular Automata Agapie, Alexandru Andreica, Anca Chira, Camelia Giuclea, Marius PLoS One Research Article Modelled as finite homogeneous Markov chains, probabilistic cellular automata with local transition probabilities in (0, 1) always posses a stationary distribution. This result alone is not very helpful when it comes to predicting the final configuration; one needs also a formula connecting the probabilities in the stationary distribution to some intrinsic feature of the lattice configuration. Previous results on the asynchronous cellular automata have showed that such feature really exists. It is the number of zero-one borders within the automaton's binary configuration. An exponential formula in the number of zero-one borders has been proved for the 1-D, 2-D and 3-D asynchronous automata with neighborhood three, five and seven, respectively. We perform computer experiments on a synchronous cellular automaton to check whether the empirical distribution obeys also that theoretical formula. The numerical results indicate a perfect fit for neighbourhood three and five, which opens the way for a rigorous proof of the formula in this new, synchronous case. Public Library of Science 2014-10-01 /pmc/articles/PMC4182702/ /pubmed/25271778 http://dx.doi.org/10.1371/journal.pone.0108177 Text en © 2014 Agapie et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Agapie, Alexandru
Andreica, Anca
Chira, Camelia
Giuclea, Marius
Predictability in Cellular Automata
title Predictability in Cellular Automata
title_full Predictability in Cellular Automata
title_fullStr Predictability in Cellular Automata
title_full_unstemmed Predictability in Cellular Automata
title_short Predictability in Cellular Automata
title_sort predictability in cellular automata
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182702/
https://www.ncbi.nlm.nih.gov/pubmed/25271778
http://dx.doi.org/10.1371/journal.pone.0108177
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