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  "body": "\n## Summary\n\nKriging is a method to predict the value of a random field at an unobserved location from its values at observed locations.\nThe prediction is the best linear unbiased predictor: the weighted average of the data with the smallest mean squared prediction error.\nThe weights come from the spatial dependence of the field, described by its [[variogram|ZF8AA2JVKV]] or its covariance function.\nIt is named after the mining engineer Danie Krige, and in the language of [[Gaussian processes|22WEM6RTW0]] it is essentially Gaussian process regression.\n\n## Explanations\n\n### L1 human\n\nSuppose you measured the gold content of rock at a few drill holes and want a guess for a new spot.\nNearby holes should count more than far ones, and holes that cluster together should not be counted twice.\nKriging chooses the weights to make the expected squared error of the guess as small as possible.\nAt a drill hole itself, kriging returns the measured value, so it interpolates the data.\nIt also gives the size of the expected error, which is larger far from the data.\n",
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