Comparer des méthodes

	Microsimulation bayésienne	Inférence bayésienne
Domaine	Simulation	Statistique
Famille	Process / pipeline	Bayesian methods
Année d'origine	1990s–2000s	1763
Auteur d'origine	Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers	Thomas Bayes; Pierre-Simon Laplace
Type	Individual-level probabilistic simulation with Bayesian updating	Probabilistic inference paradigm
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.	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Alias	Bayesian micro-simulation, BMS, Bayesian individual-level simulation, Probabilistic microsimulation	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Apparentées	6	3
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.	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.