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	Simulazione Monte Carlo Bayesiana	Simulazione Monte Carlo
Campo	Simulazione	Processo decisionale
Famiglia	Process / pipeline	MCDM
Anno di origine	1987–1990s	1949
Ideatore	O'Hagan, A. and colleagues	Metropolis, N., Ulam, S.
Tipo	Simulation / uncertainty quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminale	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	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	—
Correlati	4	0
Sintesi	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.	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.