Abstract

This talk will describe a flexible Bayesian model that can be used to predict the output of a deterministic simulator code. The model assumes that the output can be described as the sum of a smooth global trend plus deviations from the global trend. The global trend and the local deviations are modeled as draws from independent GPs with separable correlation functions subject to appropriate constraints to enforce smoothness of the global process compared with the local deviation process. The accuracy and limitations of predictions made using this model are demonstrated in a series of examples. The model is used to perform variable selection by identifying the most active inputs to the simulator. Inputs having ``smaller'' posterior distributions of the model's correlation parameters are judged to be more active. A reference inactive input is added to the data to judge the size of the correlation parameter for inactive inputs. Joint work with Casey Davis and Christopher Hans