Exact inference for Gaussian Process Regression in Case of Big Data with the Cartesian product structure (in English)

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Авторы:

Belyaev Mikhail, Burnaev Evgeny, Kapushev Yermek

Издание:

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

Абстракт:

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.

Ключевые слова: Gaussian processes, Design of experiments

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