方法对比

	贝叶斯蒙特卡洛模拟	贝叶斯系统动力学
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	1987–1990s	2000s–2010s
提出者	O'Hagan, A. and colleagues	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities
类型	Simulation / uncertainty quantification	Simulation with probabilistic parameter learning
开创性文献	O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 9780470029992	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.
别名	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics
相关	4	6
摘要	Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms.	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.