DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

Autor: Olivier Roustant, David Ginsbourger, Yves Deville

Publikováno v: Journal of Statistical Software, Vol 51, Iss 1 (2012)

We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and the optimization of expensive-to-evaluate deterministic functions. Following a self-contained mini tutorial on Kriging-based approximation and optimizat

Externí odkaz: https://doaj.org/article/2b4e0a22f9dd40cfab4ce4453bbbc6fa

Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia

Autor: Christos Davatzikos, Ahmed Abdulkadir, David Ginsbourger, Patric Wyss, Stefan Klöppel, Haochang Shou

Publikováno v: Wyss, Patric; Ginsbourger, David; Shou, Haochang; Davatzikos, Christos; Klöppel, Stefan; Abdulkadir, Ahmed (2023). Adaptive data-driven selection of sequences of biological and cognitive markers in pre-clinical diagnosis of dementia. Scientific Reports, 13(1), p. 6406. Nature Publishing Group 10.1038/s41598-023-32867-z

Combining the right—potentially invasive and expensive, markers at the appropriate time is critical to obtain reliable yet economically sustainable decisions in the preclinical diagnosis of dementia. We propose a data-driven analytical framework to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe342528d3efabedf58963ba7d1fcffa
https://doi.org/10.1101/2021.10.26.21265515

Goal-oriented adaptive sampling under random field modelling of response probability distributions

Autor: Athénaïs Gautier, David Ginsbourger, Guillaume Pirot

Publikováno v: Gautier, Athénaïs; Ginsbourger, David; Pirot, Guillaume (August 2021). Goal-oriented adaptive sampling under random field modelling of response probability distributions. ESAIM: Proceedings and Surveys, 71, pp. 89-100. EDP Sciences 10.1051/proc/202171108
University of Western Australia
ESAIM: Proceedings and Surveys, Vol 71, Pp 89-100 (2021)

In the study of natural and artificial complex systems, responses that are not completely determined by the considered decision variables are commonly modelled probabilistically, resulting in response distributions varying across decision space. We c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd8b19f8980bbe4f9539eee0a4fc121f
https://boris.unibe.ch/168972/1/proc2107108.pdf

Modeling non-stationary extreme dependence with stationary max-stable processes and multidimensional scaling

Autor: David Ginsbourger, Olivia Martius, Clément Chevalier

Publikováno v: Chevalier, Clément; Martius, Olivia; Ginsbourger, David (2020). Modeling non-stationary extreme dependence with stationary max-stable processes and multidimensional scaling. Journal of computational and graphical statistics : JCGS, 30(3), pp. 745-755. American Statistical Association 10.1080/10618600.2020.1844213

Modeling the joint distribution of extreme weather events in multiple locations is a challenging task with important applications. In this study, we use max-stable models to study extreme daily precipitation events in Switzerland. The non-stationarit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47dd0da4dffdb0f6bea8ed98deb92cfb
https://boris.unibe.ch/148264/1/chevalier_2020_a_modeling.pdf

Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding

Autor: Jean-Michel Loubes, Alexandra Suvorikova, François Bachoc, Vladimir Spokoiny, David Ginsbourger

Publikováno v: Electron. J. Statist. 14, no. 2 (2020), 2742-2772
Bachoc, François; Suvorikova, Alexandra; Ginsbourger, David; Loubes, Jean-Michel; Spokoiny, Vladimir (2020). Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding. Electronic journal of statistics, 14(2), pp. 2742-2772. Institute of Mathematical Statistics 10.1214/20-EJS1725

In this work, we investigate Gaussian Processes indexed by multidimensional distributions. While directly constructing radial positive definite kernels based on the Wasserstein distance has been proven to be possible in the unidimensional case, such

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2efbbf871105b9510e1349196aa25ff4