Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning for their nonstationary flexibility and ability to cope with abrupt regime changes in training data.…
We consider large-scale industrial computer model calibration, combining multi-output simulation with limited physical observation, involved in the development of a honeycomb seal. Toward that end,…
In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations…
A common challenge in computer experiments and related fields is to efficiently explore the input space using a small number of samples, that is, the experimental design problem. Much of the recent…
Bayesian model selection provides a natural alternative to classical hypothesis testing based on p-values. While many articles mention that Bayesian model selection can be sensitive to prior specif…