Robert B. Gramacy

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	すべて	2020 年以来
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Mickael BinoisInria Sophia Antipolis - Méditerranée確認したメールアドレス: inria.fr
Nicholas W WatkinsVisiting Professor, LSE確認したメールアドレス: lse.ac.uk
Christian L. E. FranzkeCenter for Climate Physics, Institute for Basic Science (IBS), Pusan National University確認したメールアドレス: pusan.ac.kr
Stefan M. WildBerkeley Lab, Applied Mathematics & Computational Research Division確認したメールアドレス: lbl.gov
Furong SunAmazon確認したメールアドレス: amazon.com
Sébastien Le DigabelGERAD and Polytechnique Montréal確認したメールアドレス: polymtl.ca
Mike LudkovskiProfessor, UC Santa Barbara確認したメールアドレス: pstat.ucsb.edu
Leah R. JohnsonVirginia Tech確認したメールアドレス: vt.edu
Nick PolsonProfessor of Econometrics and Statistics, University of Chicago確認したメールアドレス: chicagobooth.edu
Jiangeng HuangAbbVie確認したメールアドレス: abbvie.com
Ben HaalandUniversity of Utah確認したメールアドレス: hsc.utah.edu
Finn LindgrenUniversity of Edinburgh確認したメールアドレス: ed.ac.uk
Reinhard FurrerProfessor of Applied Statistics, University of Zurich確認したメールアドレス: math.uzh.ch
Matthias KatzfussProfessor of Statistics, University of Wisconsin–Madison確認したメールアドレス: wisc.edu
Matthew HeatonAssociate Professor, Department of Statistics, Brigham Young University確認したメールアドレス: stat.byu.edu
Abhirup DattaAssociate Professor, Biostatistics, Bloomberg School of Public Health, Johns Hopkins University確認したメールアドレス: jhu.edu
Andrew FinleyDepartment of Forestry, Michigan State University確認したメールアドレス: msu.edu
Andrew Zammit MangionUniversity of Wollongong確認したメールアドレス: uow.edu.au
Douglas NychkaProfessor, Applied Mathematics and Statistics, Colorado School of Mines確認したメールアドレス: mines.edu
Dorit HammerlingAssociate Professor of Statistics確認したメールアドレス: mines.edu

フォロー

Robert B. Gramacy

Virginia Tech

確認したメールアドレス: vt.edu - ホームページ

statistics machine learning optimization uncertainty quantification


タイトル引用回数順公開年順タイトル順	引用先引用先	年
Bayesian treed Gaussian process models with an application to computer modeling RB Gramacy, HKH Lee Journal of the American Statistical Association 103 (483), 1119-1130, 2008	971	2008
Surrogates: Gaussian process modeling, design, and optimization for the applied sciences RB Gramacy Chapman and Hall/CRC, 2020	714	2020
A case study competition among methods for analyzing large spatial data MJ Heaton, A Datta, AO Finley, R Furrer, J Guinness, R Guhaniyogi, ... Journal of Agricultural, Biological and Environmental Statistics 24, 398-425, 2019	503*	2019
Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015	462	2015
Cases for the nugget in modeling computer experiments RB Gramacy, HKH Lee Statistics and Computing 22, 713-722, 2012	354	2012
Adaptive design and analysis of supercomputer experiments RB Gramacy, HKH Lee Technometrics 51 (2), 130-145, 2009	304	2009
tgp: an R package for Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian process models RB Gramacy Journal of Statistical Software 19, 1-46, 2007	270	2007
Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models RB Gramacy, M Taddy Journal of Statistical Software 33, 1-48, 2010	263*	2010
Importance tempering R Gramacy, R Samworth, R King Statistics and Computing 20 (1), 1-7, 2010	253	2010
Practical heteroscedastic Gaussian process modeling for large simulation experiments M Binois, RB Gramacy, M Ludkovski Journal of Computational and Graphical Statistics 27 (4), 808-821, 2018	239	2018
An open challenge to advance probabilistic forecasting for dengue epidemics MA Johansson, KM Apfeldorf, S Dobson, J Devita, AL Buczak, B Baugher, ... Proceedings of the National Academy of Sciences 116 (48), 24268-24274, 2019	219	2019
Optimization under unknown constraints RB Gramacy, HKH Lee Bayesian Statistics 9 9, 229, 2011	207*	2011
A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951–1980 T Graves, R Gramacy, N Watkins, C Franzke Entropy 19 (9), 437, 2017	185	2017
laGP: large-scale spatial modeling via local approximate Gaussian processes in R RB Gramacy Journal of Statistical Software 72, 1-46, 2016	184	2016
Modeling an augmented Lagrangian for blackbox constrained optimization RB Gramacy, GA Gray, S Le Digabel, HKH Lee, P Ranjan, G Wells, ... Technometrics 58 (1), 1-11, 2016	181	2016
Dynamic trees for learning and design MA Taddy, RB Gramacy, NG Polson Journal of the American Statistical Association 106 (493), 109-123, 2011	156	2011
Particle learning of Gaussian process models for sequential design and optimization RB Gramacy, NG Polson Journal of Computational and Graphical Statistics 20 (1), 102-118, 2011	151	2011
Replication or exploration? Sequential design for stochastic simulation experiments M Binois, J Huang, RB Gramacy, M Ludkovski Technometrics 61 (1), 7-23, 2019	150	2019
ACME: Adaptive Caching Using Multiple Experts I Ari, A Amer, RB Gramacy, EL Miller, SA Brandt, DDE Long WDAS, 143-158, 2002	149	2002
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian V Picheny, RB Gramacy, S Wild, S Le Digabel Advances in neural information processing systems 29, 2016	131	2016

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