kriging
Sign in to savethumb|400px|Example of one-dimensional data interpolation by kriging, with credible intervals. Squares indicate the location of the data. The kriging interpolation, shown in red, runs along the means of the normally distributed credible intervals shown in gray. The dashed curve shows a spline that is smooth, but departs significantly from the expected values given by those means.
Article
16 sectionsContents
- Main principles
- Related terms and techniques
- Geostatistical estimator
- Linear estimation
- Methods
- Ordinary kriging{{anchor|Ordinary}}
- Simple kriging{{anchor|Simpler}}
- Bayesian kriging
- Properties
- Applications
- Design and analysis of computer experiments
- See also
- References
- Further reading
- Historical references
- Books
thumb|400px|Example of one-dimensional data interpolation by kriging, with credible intervals. Squares indicate the location of the data. The kriging interpolation, shown in red, runs along the means of the normally distributed credible intervals shown in gray. The dashed curve shows a spline that is smooth, but departs significantly from the expected values given by those means.
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. Under suitable assumptions of the prior, kriging gives the best linear unbiased prediction (BLUP) at unsampled locations. Interpolating methods based on other criteria such as smoothness (e.g., smoothing spline) may not yield the BLUP. The method is widely used in the domain of spatial analysis and computer experiments. The technique is also known as Wiener–Kolmogorov prediction, after Norbert Wiener and Andrey Kolmogorov.