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KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging

Clément Chevalier, Victor Picheny & David Ginsbourger

Résumé Several strategies relying on kriging have recently been proposed for adaptively estimating contour lines and excursion sets of functions under severely limited evaluation budget. The recently released R package KrigInv 3 is presented and offers a sound implementation of various sampling criteria for those kinds of inverse problems. KrigInv is based on the DiceKriging package, and thus benefits from a number of options concerning the underlying kriging models. Six implemented sampling criteria are detailed in a tutorial and illustrated with graphical examples. Different functionalities of KrigInv are gradually explained. Additionally, two recently proposed criteria for batch-sequential inversion are presented, enabling advanced users to distribute function evaluations in parallel on clusters or clouds of machines. Finally, auxiliary problems are discussed. These include the fine tuning of numerical integration and optimization procedures used within the computation and the optimization of the considered criteria.
   
Mots-clés Computer experiments; Gaussian process modeling; Sequential design; Probability of failure; Contour line estimation; Excursion set; Active learning
   
Citation Chevalier, C., Picheny, V., & Ginsbourger, D. (2014). KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging. Computational Statistics & Data Analysis, 71, 1021-1034.
   
Type Article de périodique (Anglais)
Date de publication 2014
Nom du périodique Computational Statistics & Data Analysis
Volume 71
Pages 1021-1034
URL http://www.sciencedirect.com/science/article/pii/S0167947...