Pope, CA, Gosling, JP orcid.org/0000-0002-4072-3022, Barber, S orcid.org/0000-0002-7611-7219 et al. (4 more authors) (Cover date: 2021) Gaussian process modeling of heterogeneity and discontinuities using Voronoi tessellations. Technometrics, 63 (1). pp. 53-63. ISSN 0040-1706
Abstract
Many methods for modeling functions over high-dimensional spaces assume global smoothness properties; such assumptions are often violated in practice. We introduce a method for modeling functions that display heterogeneity or contain discontinuities. The heterogeneity is dealt with by using a combination of Voronoi tessellation, to partition the input space, and separate Gaussian processes to model the function over different regions of the partitioned space. The proposed method is highly flexible since it allows the Voronoi cells to combine to form regions, which enables nonconvex and disconnected regions to be considered. In such problems, identifying the borders between regions is often of great importance and we propose an adaptive sampling method to gain extra information along such borders. The method is illustrated by simulated examples and an application to real data, in which we see improvements in prediction error over the commonly used stationary Gaussian process and other nonstationary variations. In our application, a computationally expensive computer model that simulates the formation of clouds is investigated, the proposed method more accurately predicts the underlying process at unobserved locations than existing emulation methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 American Statistical Association and the American Society for Quality. This is an author produced version of a paper published in Technometrics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Emulation, Gaussian process, Locating discontinuities, Voronoi tessellation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Sep 2019 09:28 |
Last Modified: | 27 Jan 2022 10:15 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/00401706.2019.1692696 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151322 |