Nested Kriging predictions for datasets with large number of observations

19 Jul 2016Didier RullièreNicolas DurrandeFrançois BachocClément Chevalier

This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often considered to tackle such a problem but the method suffers from its computational burden when the number of observation points is large... (read more)

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