In statistics, Gaussian process emulator is one name for a general type of statistical model that has been used in contexts where the problem is to make maximum use of the outputs of a complicated (often non-random) computer-based simulation model.
The variation of the outputs of the simulation model is expected to vary reasonably smoothly with the inputs, but in an unknown way.
The Gaussian process emulator model treats the problem from the viewpoint of Bayesian statistics.
In this approach, even though the output of the simulation model is fixed for any given set of inputs, the actual outputs are unknown unless the computer model is run and hence can be made the subject of a Bayesian analysis.
The model includes a description of the correlation or covariance of the outputs, which enables the model to encompass the idea that differences in the output will be small if there are only small differences in the inputs.