Error estimation properties of Gaussian process models in stochastic simulations

► Error estimation is studied in Gaussian process models built from stochastic samples. ► The relationship between prediction error and prediction variance is quantified. ► With a signal-to-noise ratio less than 10, GPM loses accuracy in error estimation. ► The GPM prediction errors are normally distributed, despite bias in GPM prediction.