قارن الطرق

	ديناميكيات النظام البايزية	محاكاة مونت كارلو
المجال	المحاكاة	اتخاذ القرار
العائلة	Process / pipeline	MCDM
سنة النشأة	2000s–2010s	1949
صاحب الطريقة	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Metropolis, N., Ulam, S.
النوع	Simulation with probabilistic parameter learning	Robustness wrapper — Monte Carlo uncertainty propagation
المصدر التأسيسي	Rahmandad, H., & Sterman, J. D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998–1014.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
الأسماء البديلة	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	—
ذات صلة	6	0
الملخص	Bayesian System Dynamics (BSD) integrates Bayesian statistical inference with causal stock-and-flow simulation models. Prior knowledge about model parameters is updated using observed time-series data to produce posterior distributions, which are then propagated through the simulation to yield probabilistic forecasts and policy evaluations rather than single deterministic trajectories.	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.