A1 Journal article (refereed)
Efficient spatial designs using Hausdorff distances and Bayesian optimization (2022)

Paglia, J., Eidsvik, J., & Karvanen, J. (2022). Efficient spatial designs using Hausdorff distances and Bayesian optimization. Scandinavian Journal of Statistics, 49(3), 1060-1084. https://doi.org/10.1111/sjos.12554

JYU authors or editors

Publication details

All authors or editors: Paglia, Jacopo; Eidsvik, Jo; Karvanen, Juha

Journal or series: Scandinavian Journal of Statistics

ISSN: 0303-6898

eISSN: 1467-9469

Publication year: 2022

Publication date: 20/09/2021

Volume: 49

Issue number: 3

Pages range: 1060-1084

Publisher: Wiley-Blackwell

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1111/sjos.12554

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/78515

Abstract

An iterative Bayesian optimisation technique is presented to find spatial designs of data that carry much information. We use the decision theoretic notion of value of information as the design criterion. Gaussian process surrogate models enable fast calculations of expected improvement for a large number of designs, while the full-scale value of information evaluations are only done for the most promising designs. The Hausdorff distance is used to model the similarity between designs in the surrogate Gaussian process covariance representation, and this allows the suggested algorithm to learn across different designs. We study properties of the Bayesian optimisation design algorithm in a synthetic example and real-world examples from forest conservation and petroleum drilling operations. In the synthetic example we consider a model where the exact solution is available and we run the algorithm under different versions of this example and compare it with existing approaches such as sequential selection and an exchange algorithm.

Keywords: spatial analysis; decision support systems; optimisation; Bayesian analysis

Free keywords: Bayesian optimisation; Hausdorff distance; value of information

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Related projects

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 2