Fast Functional Response Estimation in Computer Experiments with Nonseparable Covariance Functions

• Activity Number: 307
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Matthew Plumlee*+ and V. Roshan Joesph
• Companies: Georgia Institute of Technology and Georgia Institute of Technology
• Keywords: Gaussian Process ; Computer Experiment ; Functional Response

Abstract

Gaussian process models have become very popular to model functional responses from computer experiments. However, both the likelihood and the predictive distribution from these models requires computationally expensive matrix decomposition. Many works have included the separability assumptions on the covariance function of the Gaussian process model which eases the computational burden. This work discusses nonseparable covariance models that can be used with computationally fast algorithms.