Coupled Gaussian Process Models
Seminar Room 1, Newton Institute
Gaussian Process (GP) models are commonly employed in computer experiments for modeling deterministic functions. The model assumes second-order stationarity and therefore, the predictions can become poor when such assumptions are violated. In this work, we propose a more accurate approach by coupling two GP models together that incorporates both the non-stationarity in mean and variance. It gives better predictions when the experimental design is sparse and can also improve the prediction intervals by quantifying the change of local variability associated with the response. Advantages of the new predictor are demonstrated using several examples from the literature.