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arXiv · 1802.00045

Composite Gaussian Processes: Scalable Computation and Performance Analysis

Xiuming Liu·Dave Zachariah·Edith C. H. Ngai
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Abstract

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.

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Xiuming Liu, Dave Zachariah, Edith C. H. Ngai. 2018-01-31. Composite Gaussian Processes: Scalable Computation and Performance Analysis. https://arxiv.org/abs/1802.00045

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