Comparer des méthodes

	Automates cellulaires bayésiens	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	2000s	1949
Auteur d'origine	Multiple contributors (Bayesian calibration of CA emerged in spatial / land-use modeling literature, 2000s–2010s)	Metropolis, N., Ulam, S.
Type	Simulation — probabilistic rule inference	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	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	—
Apparentées	6	0
Résumé	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.