Efficient spatial designs using Hausdorff distances and Bayesian optimization
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
Published inScandinavian Journal of Statistics
© 2021 the Authors
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. ...
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingJacopo Paglia’s and Jo Eidsvik’s work are supported by the KPN project 255418/E30: "Reduced uncertainty in overpressures and drilling window prediction ahead of the bit (PressureAhead)", of the Norwegian Research Council and the DrillWell Centre (AkerBP, Wintershall, ConocoPhillips and Equinor). Juha Karvanen’s work is supported by Grant number 311877 "Decision analytics utilizing causal models and multiobjective optimisation" (DEMO), of the Academy of Finland ...
Showing items with similar title or keywords.
Heikkinen, Risto; Sipilä, Juha; Ojalehto, Vesa; Miettinen, Kaisa (Inderscience Publishers, 2022)We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. ...
Rahat, Alma; Chugh, Tinkle; Fieldsend, Jonathan; Allmendinger, Richard; Miettinen, Kaisa (Springer International Publishing, 2022)Many methods for performing multi-objective optimisation of computationally expensive problems have been proposed recently. Typically, a probabilistic surrogate for each objective is constructed from an initial dataset. ...
Eyvindson, Kyle; Hakanen, Jussi; Mönkkönen, Mikko; Juutinen, Artti; Karvanen, Juha (Springer Berlin Heidelberg, 2019)Developing environmental conservation plans involves assessing trade-offs between the benefits and costs of conservation. The benefits of conservation can be established with ecological inventories or estimated based on ...
Identifying territories using presence-only citizen science data : An application to the Finnish wolf population Karppinen, Santeri; Rajala, Tuomas; Mäntyniemi, Samu; Kojola, Ilpo; Vihola, Matti (Elsevier BV, 2022)Citizens, community groups and local institutions participate in voluntary biological monitoring of population status and trends by providing species data e.g. for regulations and conservation. Sophisticated statistical ...
Myllymäki, Mari (University of Jyväskylä, 2009)