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	Automi Cellulari Bayesiani	Simulazione Monte Carlo
Campo	Simulazione	Processo decisionale
Famiglia	Process / pipeline	MCDM
Anno di origine	2000s	1949
Ideatore	Multiple contributors (Bayesian calibration of CA emerged in spatial / land-use modeling literature, 2000s–2010s)	Metropolis, N., Ulam, S.
Tipo	Simulation — probabilistic rule inference	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminale	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.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	BCA, Bayesian CA, Probabilistic Cellular Automata (Bayesian), Bayes-calibrated CA	—
Correlati	6	0
Sintesi	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.