পদ্ধতির তুলনা করুন

	অনিশ্চয়তা পরিমাপ (Uncertainty Quantification)	ক্রিগিং স্থানিক ইন্টারপোলেশন
ক্ষেত্র	অনুকরণ	স্থানিক বিশ্লেষণ
পরিবার	Process / pipeline	Regression model
উদ্ভবের বছর	Seminal modern form: 2002	1963
প্রবর্তক	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)	Georges Matheron (formalised geostatistics)
ধরন	Computational uncertainty analysis framework	Geostatistical spatial interpolation
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.	Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266.
অপর নাম	UQ, polynomial chaos expansion, PCE, Kriging surrogate	geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon)
সম্পর্কিত	9	5
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.	Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms.