Comparer des méthodes

	Microsimulation bayésienne	Modèle de Markov
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s	1906
Auteur d'origine	Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers	Andrei Markov
Type	Individual-level probabilistic simulation with Bayesian updating	Probabilistic state-transition model
Source fondatrice	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
Alias	Bayesian micro-simulation, BMS, Bayesian individual-level simulation, Probabilistic microsimulation	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
Apparentées	6	5
Résumé	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.