Σύγκριση μεθόδων

	Προσομοίωση Monte Carlo με Bayesian	Ανάλυση Ευαισθησίας Bayes
Πεδίο	Προσομοίωση	Προσομοίωση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1987–1990s	1984–1994
Δημιουργός	O'Hagan, A. and colleagues	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)
Τύπος	Simulation / uncertainty quantification	Uncertainty propagation and sensitivity quantification
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis
Συναφείς	4	5
Σύνοψη	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.