Comparer des méthodes

	Simulation bayésienne de Monte-Carlo	Analyse de Sensibilité Bayésienne
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1987–1990s	1984–1994
Auteur d'origine	O'Hagan, A. and colleagues	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)
Type	Simulation / uncertainty quantification	Uncertainty propagation and sensitivity quantification
Source fondatrice	O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 9780470029992	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.
Alias	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis
Apparentées	4	5
Résumé	Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms.	Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty.