Robert B. Gramacy
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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
Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy
Chapman and Hall/CRC, 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
Local Gaussian process approximation for large computer experiments
RB Gramacy, DW Apley
Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015
Cases for the nugget in modeling computer experiments
RB Gramacy, HKH Lee
Statistics and Computing 22, 713-722, 2012
Adaptive design and analysis of supercomputer experiments
RB Gramacy, HKH Lee
Technometrics 51 (2), 130-145, 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
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
Importance tempering
R Gramacy, R Samworth, R King
Statistics and Computing 20 (1), 1-7, 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
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
Optimization under unknown constraints
RB Gramacy, HKH Lee
Bayesian Statistics 9 9, 229, 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
laGP: large-scale spatial modeling via local approximate Gaussian processes in R
RB Gramacy
Journal of Statistical Software 72, 1-46, 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
Dynamic trees for learning and design
MA Taddy, RB Gramacy, NG Polson
Journal of the American Statistical Association 106 (493), 109-123, 2011
ACME: Adaptive Caching Using Multiple Experts
I Ari, A Amer, RB Gramacy, EL Miller, SA Brandt, DDE Long
WDAS, 143-158, 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
Replication or exploration? Sequential design for stochastic simulation experiments
M Binois, J Huang, RB Gramacy, M Ludkovski
Technometrics 61 (1), 7-23, 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
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