Módszerek összehasonlítása

	Bayesian Monte Carlo szimuláció	MONTE-CARLO-SIMULATION
Tudományterület	Szimuláció	Döntéshozatal
Módszercsalád	Process / pipeline	MCDM
Keletkezés éve	1987–1990s	1949
Megalkotó	O'Hagan, A. and colleagues	Metropolis, N., Ulam, S.
Típus	Simulation / uncertainty quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Alapmű	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
Alternatív nevek	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	—
Kapcsolódó	4	0
Összefoglaló	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.