Сравнение на методи

	Байесови клетъчни автомати	Байесовско моделиране, базирано на агенти
Област	Симулационно моделиране	Симулационно моделиране
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2000s	2000s–2010s
Създател	Multiple contributors (Bayesian calibration of CA emerged in spatial / land-use modeling literature, 2000s–2010s)	Sunnaker et al. / Grazzini & Richiardi (among key contributors)
Тип	Simulation — probabilistic rule inference	Simulation calibration and inference framework
Основополагащ източник	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.	Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803.
Други названия	BCA, Bayesian CA, Probabilistic Cellular Automata (Bayesian), Bayes-calibrated CA	Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation
Свързани	6	5
Резюме	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.	Bayesian Agent-Based Modeling integrates Bayesian statistical inference with agent-based simulation to calibrate model parameters and quantify uncertainty. Rather than fixing agent rules and parameters by assumption, this approach treats unknown parameters as probability distributions and updates them systematically against observed data, yielding a full posterior over plausible model configurations.