Abstract: The Gaussian process regression model is a popular type of “emulator” used as a fast surrogate for computationally expensive simulators (deterministic computer models). For simulators with multivariate output, common practice is to specify a separable covariance structure for the Gaussian process. Though computationally convenient, this can be too restrictive, leading to poor performance of the emulator, particularly when the different simulator outputs represent different physical quantities. Also, treating the simulator outputs as indepen...
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Topics: 
Algorithm
Mathematical optimization