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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Gaussian Process Regression for Structured Data Sets
verfasst von : Mikhail Belyaev, Evgeny Burnaev, Yermek Kapushev
Erschienen in: Statistical Learning and Data Sciences
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Approximation algorithms are widely used in many engineering problems. To obtain a data set for approximation a factorial design of experiments is often used. In such case the size of the data set can be very large. Therefore, one of the most popular algorithms for approximation — Gaussian Process regression — can hardly be applied due to its computational complexity. In this paper a new approach for a Gaussian Process regression in case of a factorial design of experiments is proposed. It allows to efficiently compute exact inference and handle large multidimensional and anisotropic data sets.