Composite Gaussian Processes: Scalable Computation and Performance Analysis
Xiuming Liu, Dave Zachariah, Edith C.‐H. Ngai · arXiv (Cornell University) · 2018
Gaussian process (GP) models provide a powerful tool for prediction but are computationally prohibitive using large data sets. In such scenarios, one has to resort to approximate methods. We derive an approximation based on a composite likelihood approach using a general belief updating framework, which leads to a recursive computation of the predictor as well as of learning the hyper-parameters. We then provide an analysis of the derived composite GP model in predictive and information-theoretic terms. Finally, we evaluate the approximation with both synthetic data and a real-world application.