방법 비교

	베이즈 미시 시뮬레이션	마르코프 모델
분야	시뮬레이션	시뮬레이션
계열	Process / pipeline	Process / pipeline
기원 연도	1990s–2000s	1906
창시자	Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers	Andrei Markov
유형	Individual-level probabilistic simulation with Bayesian updating	Probabilistic state-transition model
원전	Williamson, P., Birkin, M., & Rees, P. H. (2000). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30(5), 785-816.	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
별칭	Bayesian micro-simulation, BMS, Bayesian individual-level simulation, Probabilistic microsimulation	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
관련	6	5
요약	Bayesian Microsimulation combines individual-level simulation of heterogeneous populations with Bayesian statistical inference. Each synthetic individual follows a probabilistic life path, while model parameters are governed by prior beliefs updated with observed data. This approach is widely used in health technology assessment, public policy costing, and demographic projection, where uncertainty in both model inputs and structural assumptions must be formally quantified and propagated through to output estimates.	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.