Springer Probabilistic Cellular Automata: Theory, Applications & Perspectives

This comprehensive volume examines Probabilistic Cellular Automata (PCA) through the lenses of statistical mechanics, probability theory, computational biology and computer science. PCA extend classical Cellular Automata models by incorporating random updating rules, creating powerful computing tools for intricate numerical problems and realistic simulations of systems driven by interactions among numerous neighbouring structures. The book, part of the Emergence, Complexity and Computation series (volume 27), covers PCA applications across diverse fields from pure probability through practical computational domains. Ideal for researchers, mathematicians and computer scientists seeking to understand how probabilistic methods enhance cellular automaton frameworks for modelling complex phenomena and solving real-world computational challenges.