Comparar métodos

	Dinâmica de Sistemas Bayesiana	Modelo Bayesiano de Markov
Área	Simulação	Simulação
Família	Process / pipeline	Process / pipeline
Ano de origem	2000s–2010s	1990s–2000s
Autor original	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community
Tipo	Simulation with probabilistic parameter learning	Probabilistic state-transition simulation
Fonte seminal	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
Outros nomes	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation
Relacionados	6	4
Resumo	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.