방법 비교

	베이즈 시스템 다이내믹스	마르코프 모델
분야	시뮬레이션	시뮬레이션
계열	Process / pipeline	Process / pipeline
기원 연도	2000s–2010s	1906
창시자	Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities	Andrei Markov
유형	Simulation with probabilistic parameter learning	Probabilistic state-transition model
원전	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
별칭	BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
관련	6	5
요약	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.