So sánh phương pháp

	Mô phỏng Monte Carlo Bayes	Mô phỏng Monte Carlo
Lĩnh vực	Mô phỏng	Ra quyết định
Họ	Process / pipeline	MCDM
Năm ra đời	1987–1990s	1949
Người khởi xướng	O'Hagan, A. and colleagues	Metropolis, N., Ulam, S.
Loại	Simulation / uncertainty quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Công trình gốc	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
Tên gọi khác	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation	—
Liên quan	4	0
Tóm tắt	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.