方法对比

	贝叶斯系统动力学	贝叶斯马尔可夫模型
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	2000s–2010s	1990s–2000s
提出者	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community
类型	Simulation with probabilistic parameter learning	Probabilistic state-transition simulation
开创性文献	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.	Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629
别名	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation
相关	6	4
摘要	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.	A Bayesian Markov model is a state-transition simulation method that combines Markov chain cohort modeling with Bayesian statistical inference. By placing prior distributions on transition probabilities and updating them with observed data, the approach propagates full parameter uncertainty through the simulation, yielding posterior distributions over outcomes such as costs, life-years, or quality-adjusted life-years rather than single-point estimates.