Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Automați Celulari Stocastici× | Model Markov× | |
|---|---|---|
| Domeniu | Simulare | Simulare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1940s–1980s | 1906 |
| Autorul original≠ | von Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B. | Andrei Markov |
| Tip≠ | Grid-based stochastic simulation | Probabilistic state-transition model |
| Sursa seminală≠ | Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 9781579550080 | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Denumiri alternative | SCA, Probabilistic Cellular Automata, PCA, Stochastic CA | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Înrudite | 5 | 5 |
| Rezumat≠ | Stochastic Cellular Automata (SCA) extend classical cellular automata by replacing deterministic transition rules with probabilistic ones, allowing each cell on a grid to change state according to a probability distribution conditioned on its neighborhood. This makes SCA a powerful tool for simulating real-world spatial processes where randomness, noise, and uncertainty govern local interactions — from epidemic spread and forest fires to traffic flow and material diffusion. | A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling. |
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