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Most surrogate models for computer experiments are interpolators, and themost common interpolator is a Gaussian process (GP) that deliberately omits asmall-scale (measurement) error term called the nugget. The explanation is thatcomputer experiments are, by definition, "deterministic", and so there is nomeasurement error. We think this is too narrow a focus for a computerexperiment and a statistically inefficient way to model them. We show thatestimating a (non-zero) nugget can lead to surrogate models with betterstatistical properties, such as predictive accuracy and coverage, in a varietyof common situations.

Cases for the nugget in modeling computer experiments