Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bejeza šūnu automāti× | Aģentu balstīta šūnu automāti× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s | 1986–1996 |
| Autors≠ | Multiple contributors (Bayesian calibration of CA emerged in spatial / land-use modeling literature, 2000s–2010s) | Wolfram, S.; Epstein, J. M. & Axtell, R. |
| Tips≠ | Simulation — probabilistic rule inference | Hybrid spatial simulation |
| Pirmavots≠ | Hosseinali, F., Alesheikh, A. A., Nourian, F. (2013). Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city. Cities, 31, 105-113. DOI ↗ | Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 978-1579550080 |
| Citi nosaukumi | BCA, Bayesian CA, Probabilistic Cellular Automata (Bayesian), Bayes-calibrated CA | ABCA, CA-ABM, Agent-CA, Hybrid Agent-Cellular Automaton |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Bayesian Cellular Automata (BCA) couples the local-rule spatial dynamics of classical cellular automata with Bayesian inference to learn or calibrate transition probabilities from observed data. Rather than fixing rules by hand, the analyst encodes prior knowledge about how cells change state and updates those beliefs with empirical evidence, producing a posterior distribution over rule parameters that drives principled uncertainty-aware simulation. | Agent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-based modeling. Cells in a spatial grid both evolve according to neighborhood rules and host agents that perceive, decide, and act, enabling the study of complex spatial phenomena such as land-use change, disease spread, crowd dynamics, and ecosystem evolution. |
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