Comparar métodos

	Microsimulación bayesiana	Modelo de Markov
Campo	Simulación	Simulación
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s–2000s	1906
Autor original	Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers	Andrei Markov
Tipo	Individual-level probabilistic simulation with Bayesian updating	Probabilistic state-transition model
Fuente seminal	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
Relacionados	6	5
Resumen	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.