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	Bayesian Microsimulation	मोंटे कार्लो सिमुलेशन
क्षेत्र	अनुकरण	निर्णयन
परिवार	Process / pipeline	MCDM
उद्भव वर्ष	1990s–2000s	1949
प्रवर्तक	Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers	Metropolis, N., Ulam, S.
प्रकार	Individual-level probabilistic simulation with Bayesian updating	Robustness wrapper — Monte Carlo uncertainty propagation
मौलिक स्रोत	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.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
उपनाम	Bayesian micro-simulation, BMS, Bayesian individual-level simulation, Probabilistic microsimulation	—
संबंधित	6	0
सारांश	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.