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Gaussian process regression (GPR) is a non-parametric Bayesian technique forinterpolating or fitting data. The main barrier to further uptake of thispowerful tool rests in the computational costs associated with the matriceswhich arise when dealing with large data sets. Here, we derive some simpleresults which we have found useful for speeding up the learning stage in theGPR algorithm, and especially for performing Bayesian model comparison betweendifferent covariance functions. We apply our techniques to both synthetic andreal data and quantify the speed-up relative to using nested sampling tonumerically evaluate model evidences.

Fast methods for training Gaussian processes on large data sets