Exact Inference for Gaussian Process Regression in Case of Big Data with the Cartesian Product Structure

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Belyaev Mikhail, Burnaev Evgeny, Kapushev Yermek


ICML 2014 workshop on New Learning Frameworks and Models for Big Data, Beijing 2014


In this paper a new approach for Gaussian Process regression in case of factorial design of experiments is proposed. It allows to efficiently compute exact inference and handle large multidimensional data sets. This functionnality is implemented in MACROS technology.

Keywords: Design of Experiments, Approximation, Gaussian Processes


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