Comparar métodos

	Simulação de Monte Carlo Bayesiana	Dinâmica de Sistemas Bayesiana
Área	Simulação	Simulação
Família	Process / pipeline	Process / pipeline
Ano de origem	1987–1990s	2000s–2010s
Autor original	O'Hagan, A. and colleagues	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities
Tipo	Simulation / uncertainty quantification	Simulation with probabilistic parameter learning
Fonte seminal	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.
Outros nomes	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics
Relacionados	4	6
Resumo	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.