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Bajezijanska Monte Karlo simulacija×Simulacija Monte Karlo×
OblastSimulacijaDonošenje odluka
PorodicaProcess / pipelineMCDM
Godina nastanka1987–1990s1949
TvoracO'Hagan, A. and colleaguesMetropolis, N., Ulam, S.
TipSimulation / uncertainty quantificationRobustness wrapper — Monte Carlo uncertainty propagation
Temeljni izvorO'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: 9780470029992Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Drugi naziviBayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation
Srodne40
SažetakBayesian 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.
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ScholarGateUporedite metode: Bayesian Monte Carlo Simulation · MONTE-CARLO-SIMULATION. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare