Robert B. Gramacy

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Mickael BinoisInria Sophia Antipolis - MéditerranéeVerified email at inria.fr
Nicholas W WatkinsVisiting Professor, LSEVerified email at lse.ac.uk
Christian L. E. FranzkeCenter for Climate Physics, Institute for Basic Science (IBS)Verified email at pusan.ac.kr
Furong SunAmazonVerified email at amazon.com
Stefan M. WildBerkeley Lab, Applied Mathematics & Computational Research DivisionVerified email at lbl.gov
Leah R. JohnsonVirginia TechVerified email at vt.edu
Sébastien Le DigabelGERAD and Polytechnique MontréalVerified email at polymtl.ca
Nick PolsonProfessor of Econometrics and Statistics, University of ChicagoVerified email at chicagobooth.edu
Mike LudkovskiProfessor, UC Santa BarbaraVerified email at pstat.ucsb.edu
Jiangeng HuangGenentechVerified email at gene.com
Ben HaalandUniversity of UtahVerified email at hsc.utah.edu
Finn LindgrenUniversity of EdinburghVerified email at ed.ac.uk
Florian GerberDepartment of Applied Mathematics and Statistics, Colorado School of MinesVerified email at mines.edu
Reinhard FurrerProfessor of Applied Statistics, University of ZurichVerified email at math.uzh.ch
Matthias KatzfussProfessor of Statistics, Texas A&M UniversityVerified email at stat.tamu.edu
Matthew HeatonAssociate Professor, Department of Statistics, Brigham Young UniversityVerified email at stat.byu.edu
Abhirup DattaAssociate Professor, Biostatistics, Bloomberg School of Public Health, Johns Hopkins UniversityVerified email at jhu.edu
Andrew FinleyDepartment of Forestry, Michigan State UniversityVerified email at msu.edu
Andrew Zammit MangionUniversity of WollongongVerified email at uow.edu.au
Douglas NychkaProfessor, Applied Mathematics and Statistics, Colorado School of MinesVerified email at mines.edu

Robert B. Gramacy

Virginia Tech

Verified email at vt.edu - Homepage

statistics machine learning optimization uncertainty quantification


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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	777	2008
Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015	370	2015
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	365*	2019
Surrogates: Gaussian process modeling, design, and optimization for the applied sciences RB Gramacy CRC press, 2020	344	2020
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	336*	2007
Cases for the nugget in modeling computer experiments RB Gramacy, HKH Lee Statistics and Computing 22, 713-722, 2012	301	2012
Adaptive design and analysis of supercomputer experiments RB Gramacy, HKH Lee Technometrics 51 (2), 130-145, 2009	264	2009
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	239*	2010
Importance tempering R Gramacy, R Samworth, R King Statistics and Computing 20 (1), 1-7, 2010	234	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	184	2018
Optimization under unknown constraints RB Gramacy, HKH Lee Bayesian Statistics 9 9, 229, 2011	163*	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	157	2017
laGP: large-scale spatial modeling via local approximate Gaussian processes in R RB Gramacy Journal of Statistical Software 72, 1-46, 2016	145	2016
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	144	2019
Dynamic trees for learning and design MA Taddy, RB Gramacy, NG Polson Journal of the American Statistical Association 106 (493), 109-123, 2011	141	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	139	2011
ACME: Adaptive Caching Using Multiple Experts I Ari, A Amer, RB Gramacy, EL Miller, SA Brandt, DDE Long WDAS, 143-158, 2002	135	2002
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	134	2016
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	111*	2008
Replication or exploration? Sequential design for stochastic simulation experiments M Binois, J Huang, RB Gramacy, M Ludkovski Technometrics 61 (1), 7-23, 2019	102	2019

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