Computationally efficient algorithm for Gaussian process regression in case of structured samples

DOI10.1134/S0965542516040163zbMath1403.62141MaRDI QIDQ327224

Evgeny Burnaev, Mikhail Belyaev, Y. Kapushev

Publication date: 19 October 2016

Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)

zbMATH Keywords

regularizationGaussian processesfactorial design of experimentsincomplete factorial design of experimentsoperations with tensors

Mathematics Subject Classification ID

Gaussian processes (60G15) General nonlinear regression (62J02) Factorial statistical designs (62K15)

Related Items (4)

Large scale variable fidelity surrogate modeling ⋮ Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions ⋮ Tensor Completion via Gaussian Process--Based Initialization ⋮ Altering Gaussian process to Student-t process for maximum distribution construction

Uses Software

• GPML
• AS 312

Cites Work

• Tensor Decompositions and Applications
• Multivariate adaptive regression splines
• Polynomial splines and their tensor products in extended linear modeling. (With discussions)
• Fast and Exact Simulation of Stationary Gaussian Processes through Circulant Embedding of the Covariance Matrix
• An Algorithm for Simulating Stationary Gaussian Random Fields
• Computationally efficient restricted maximum likelihood estimation of generalized covariance functions
• Benchmarking optimization software with performance profiles.
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