পদ্ধতির তুলনা করুন

	অনিশ্চয়তা পরিমাপ (Uncertainty Quantification)	মন্টে কার্লো সিমুলেশন
ক্ষেত্র	অনুকরণ	সিদ্ধান্ত গ্রহণ
পরিবার	Process / pipeline	MCDM
উদ্ভবের বছর	Seminal modern form: 2002	1949
প্রবর্তক	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)	Metropolis, N., Ulam, S.
ধরন	Computational uncertainty analysis framework	Robustness wrapper — Monte Carlo uncertainty propagation
মৌলিক উৎস	Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
অপর নাম	UQ, polynomial chaos expansion, PCE, Kriging surrogate	—
সম্পর্কিত	9	0
সারসংক্ষেপ	Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two primary strategies: Polynomial Chaos Expansion (PCE), which represents the model output as a series of orthogonal polynomials matched to the input distributions, and Kriging (Gaussian process) surrogates, which replace an expensive simulation with a fast statistical approximation fitted to a small set of carefully chosen runs.	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.