Robert B. Gramacy 个人学术档案

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	总计	2019 年至今
引用	7834	5014
h 指数	44	35
i10 指数	74	64

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

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Robert B. Gramacy

Virginia Tech

在 vt.edu 的电子邮件经过验证 - 首页

statistics machine learning optimization uncertainty quantification


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	934	2008
Surrogates: Gaussian process modeling, design, and optimization for the applied sciences RB Gramacy Chapman and Hall/CRC, 2020	563	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	455*	2019
Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015	425	2015
Cases for the nugget in modeling computer experiments RB Gramacy, HKH Lee Statistics and Computing 22, 713-722, 2012	334	2012
Adaptive design and analysis of supercomputer experiments RB Gramacy, HKH Lee Technometrics 51 (2), 130-145, 2009	288	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	259	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	252*	2010
Importance tempering R Gramacy, R Samworth, R King Statistics and Computing 20 (1), 1-7, 2010	244	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	212	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	199	2019
Optimization under unknown constraints RB Gramacy, HKH Lee Bayesian Statistics 9 9, 229, 2011	187*	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	178	2017
laGP: large-scale spatial modeling via local approximate Gaussian processes in R RB Gramacy Journal of Statistical Software 72, 1-46, 2016	174	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	163	2016
Dynamic trees for learning and design MA Taddy, RB Gramacy, NG Polson Journal of the American Statistical Association 106 (493), 109-123, 2011	150	2011
ACME: Adaptive Caching Using Multiple Experts I Ari, A Amer, RB Gramacy, EL Miller, SA Brandt, DDE Long WDAS, 143-158, 2002	147	2002
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	144	2011
Replication or exploration? Sequential design for stochastic simulation experiments M Binois, J Huang, RB Gramacy, M Ludkovski Technometrics 61 (1), 7-23, 2019	136	2019
Geometry: mesh generation and surface tesselation R Grasman, RB Gramacy R package version 0.1-4. URL http://cran. r-project. org/web/packages/geometry, 2008	121*	2008

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