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10.1089/cmb.2014.0074 http://scihub22266oqcxt.onion/10.1089/cmb.2014.0074 C4148062!4148062!24999557     free     free     free       J+Comput+Biol 2014 ; 21 (9): 699-708 Nephropedia Template TP {{tp|p=24999557|t=2014. Probabilistic Cellular Automata |pdf=|usr=}}{{24999557}} gab.com Text Probabilistic Cellular Automata JO: J Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=24999557 #FOAvip Cellular automata are binary lattices used for modeling complex dynamical systems. The automaton evolves iteratively from one configuration to another, using some local transition rule based on the number of ones in the neighborhood of each cell. With respect to the number of cells allowed to change per iteration, we speak of either synchronous or asynchronous automata. If randomness is involved to some degree in the transition rule, we speak of probabilistic automata, otherwise they are called deterministic. With either type of cellular automaton we are dealing with, the main theoretical challenge stays the same: starting from an arbitrary initial configuration, predict (with highest accuracy) the end configuration. If the automaton is deterministic, the outcome simplifies to one of two configurations, all zeros or all ones. If the automaton is probabilistic, the whole process is modeled by a finite homogeneous Markov chain, and the outcome is the corresponding stationary distribution. Based on our previous results for the asynchronous case?connecting the probability of a configuration in the stationary distribution to its number of zero-one borders?the article offers both numerical and theoretical insight into the long-term behavior of synchronous cellular automata. Twit Text FOAVip Probabilistic Cellular Automata ????J Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=24999557 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24999557 #FOAvip English Wikipedia <ref>{{Cite pmid|24999557}}</ref> Probabilistic Cellular Automata #MMPMID24999557 Agapie A; Andreica A; Giuclea M J Comput Biol 2014[Sep]; 21 (9): 699-708 PMID24999557show ga Cellular automata are binary lattices used for modeling complex dynamical systems. The automaton evolves iteratively from one configuration to another, using some local transition rule based on the number of ones in the neighborhood of each cell. With respect to the number of cells allowed to change per iteration, we speak of either synchronous or asynchronous automata. If randomness is involved to some degree in the transition rule, we speak of probabilistic automata, otherwise they are called deterministic. With either type of cellular automaton we are dealing with, the main theoretical challenge stays the same: starting from an arbitrary initial configuration, predict (with highest accuracy) the end configuration. If the automaton is deterministic, the outcome simplifies to one of two configurations, all zeros or all ones. If the automaton is probabilistic, the whole process is modeled by a finite homogeneous Markov chain, and the outcome is the corresponding stationary distribution. Based on our previous results for the asynchronous case?connecting the probability of a configuration in the stationary distribution to its number of zero-one borders?the article offers both numerical and theoretical insight into the long-term behavior of synchronous cellular automata. äProbabilistic Cellular Automata (epmc).pdf DeepDyve Pubget Overpricing