iopmarketing.blogg.se - Ez kriging software

Even so, they are useful in different frameworks: kriging is made for estimation of a single realization of a random field, while regression models are based on multiple observations of a multivariate data set. Both theories derive a best linear unbiased estimator based on assumptions on covariances, make use of Gauss–Markov theorem to prove independence of the estimate and error, and use very similar formulae. The method is closely related to regression analysis. Kriging predicts the value of a function at a given point by computing a weighted average of the known values of the function in the neighborhood of the point. Main principles Related terms and techniques

3.1 Design and analysis of computer experiments.

Though computationally intensive in its basic formulation, kriging can be scaled to larger problems using various approximation methods. The word is sometimes capitalized as Kriging in the literature. The English verb is to krige, and the most common noun is kriging both are often pronounced with a hard "g", following an Anglicized pronunciation of the name "Krige". Krige sought to estimate the most likely distribution of gold based on samples from a few boreholes. Krige, the pioneering plotter of distance-weighted average gold grades at the Witwatersrand reef complex in South Africa. The theoretical basis for the method was developed by the French mathematician Georges Matheron in 1960, based on the master's thesis of Danie G. The technique is also known as Wiener–Kolmogorov prediction, after Norbert Wiener and Andrey Kolmogorov.

The method is widely used in the domain of spatial analysis and computer experiments. Interpolating methods based on other criteria such as smoothness (e.g., smoothing spline) may not yield the BLUP. Under suitable assumptions of the prior, kriging gives the best linear unbiased prediction (BLUP) at unsampled locations. In statistics, originally in geostatistics, kriging or Kriging, also known as Gaussian process regression, is a method of interpolation based on Gaussian process governed by prior covariances.

The dashed curve shows a spline that is smooth, but departs significantly from the expected values given by those means. The kriging interpolation, shown in red, runs along the means of the normally distributed credible intervals shown in gray. Squares indicate the location of the data. Example of one-dimensional data interpolation by kriging, with credible intervals.