Salīdzināt metodes

	Baijesa sistēmdinamika	Bayesiešu Markovas modelis
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2010s	1990s–2000s
Autors	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community
Tips	Simulation with probabilistic parameter learning	Probabilistic state-transition simulation
Pirmavots	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
Citi nosaukumi	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation
Saistītās	6	4
Kopsavilkums	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.