Robert B. Gramacy

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	Wszystkie	Od 2020
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Mickael BinoisInria Sophia Antipolis - MéditerranéeZweryfikowany adres z inria.fr
Nicholas W WatkinsVisiting Professor, LSEZweryfikowany adres z lse.ac.uk
Christian L. E. FranzkeCenter for Climate Physics, Institute for Basic Science (IBS), Pusan National UniversityZweryfikowany adres z pusan.ac.kr
Stefan M. WildBerkeley Lab, Applied Mathematics & Computational Research DivisionZweryfikowany adres z lbl.gov
Furong SunAmazonZweryfikowany adres z amazon.com
Sébastien Le DigabelGERAD and Polytechnique MontréalZweryfikowany adres z polymtl.ca
Mike LudkovskiProfessor, UC Santa BarbaraZweryfikowany adres z pstat.ucsb.edu
Leah R. JohnsonVirginia TechZweryfikowany adres z vt.edu
Nick PolsonProfessor of Econometrics and Statistics, University of ChicagoZweryfikowany adres z chicagobooth.edu
Jiangeng HuangAbbVieZweryfikowany adres z abbvie.com
Ben HaalandUniversity of UtahZweryfikowany adres z hsc.utah.edu
Finn LindgrenUniversity of EdinburghZweryfikowany adres z ed.ac.uk
Reinhard FurrerProfessor of Applied Statistics, University of ZurichZweryfikowany adres z math.uzh.ch
Matthias KatzfussProfessor of Statistics, University of Wisconsin–MadisonZweryfikowany adres z wisc.edu
Matthew HeatonAssociate Professor, Department of Statistics, Brigham Young UniversityZweryfikowany adres z stat.byu.edu
Abhirup DattaAssociate Professor, Biostatistics, Bloomberg School of Public Health, Johns Hopkins UniversityZweryfikowany adres z jhu.edu
Andrew FinleyDepartment of Forestry, Michigan State UniversityZweryfikowany adres z msu.edu
Andrew Zammit MangionAssociate Professor, School of Mathematics and Applied Statistics, University of WollongongZweryfikowany adres z uow.edu.au
Douglas NychkaProfessor, Applied Mathematics and Statistics, Colorado School of MinesZweryfikowany adres z mines.edu
Dorit HammerlingAssociate Professor of StatisticsZweryfikowany adres z mines.edu

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Robert B. Gramacy

Virginia Tech

Zweryfikowany adres z vt.edu - Strona główna

statistics machine learning optimization uncertainty quantification


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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	965	2008
Surrogates: Gaussian process modeling, design, and optimization for the applied sciences RB Gramacy Chapman and Hall/CRC, 2020	689	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	508*	2019
Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015	461	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	307	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	269*	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	241	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	223	2019
Optimization under unknown constraints RB Gramacy, HKH Lee Bayesian Statistics 9 9, 229, 2011	212*	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	182	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	158	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	152	2011
ACME: Adaptive Caching Using Multiple Experts I Ari, A Amer, RB Gramacy, EL Miller, SA Brandt, DDE Long WDAS, 143-158, 2002	149	2002
Replication or exploration? Sequential design for stochastic simulation experiments M Binois, J Huang, RB Gramacy, M Ludkovski Technometrics 61 (1), 7-23, 2019	147	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	129*	2008
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	128	2016

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