> Cool, good to know! > > On the name though --- "eval_MSE" is a nonstandard term for "variance" > no? MSE usually refers to a loss criterion, for comparing predictions > with targets. > MSE stands for "mean squared error". The GP predictor indeed ensures minimum prediction variance (aka the mean squared error of the best linear unbiased predictor). See the book by Santner et al., 2003 "Design and analysis of computer experiments" for the fundamental theorem of prediction that establishes a strict equivalence between the GP predictor viewed as the distribution of the prediction conditional onto the observations (as exposed in Rasmussen & Williams' book) and the "best linear unbiased predictors" (commonly used in geostatistics and know as the Kriging predictors). Santner's book can be partly previewed on Google books. I used it in my thesis to introduce Gaussian process predictors. I'll let you know as soon as it's available for download (before x-mas).
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