Linganisha mbinu

	Mifumo ya Mfumo wa Bayesian	Uiguzi wa Monte Carlo
Nyanja	Uigaji	Ufanyaji Maamuzi
Familia	Process / pipeline	MCDM
Mwaka wa asili	2000s–2010s	1949
Mwanzilishi	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Metropolis, N., Ulam, S.
Aina	Simulation with probabilistic parameter learning	Robustness wrapper — Monte Carlo uncertainty propagation
Chanzo asilia	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
Majina mbadala	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	—
Zinazohusiana	6	0
Muhtasari	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.