Salīdzināt metodes

	Baijesa sistēmdinamika	Markov Model
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2010s	1906
Autors	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Andrei Markov
Tips	Simulation with probabilistic parameter learning	Probabilistic state-transition model
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.	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
Citi nosaukumi	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Saistītās	6	5
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 Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.