Prediction for Computer Experiments Having Quantitative and Qualitative Input Variables

Abstract

This article introduces a Bayesian methodology for the prediction for computer experiments having quantitative and qualitative inputs. The proposed model is a hierarchical Bayesian model with conditional Gaussian stochastic process components. For each of the qualitative inputs, our model assumes that the outputs corresponding to different levels of the qualitative input have “similar” functional behavior in the quantitative inputs. The predictive accuracy of this method is compared with the predictive accuracies of alternative proposals in examples. The method is illustrated in a biomechanical engineering application.