| 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 | 962 | 2008 |
| Cases for the nugget in modeling computer experiments RB Gramacy, HKH Lee Statistics and Computing 22, 713-722, 2012 | 346 | 2012 |
| Adaptive design and analysis of supercomputer experiments RB Gramacy, HKH Lee Technometrics 51 (2), 130-145, 2009 | 300 | 2009 |
| Nonparametric dark energy reconstruction from supernova data T Holsclaw, U Alam, B Sanso, H Lee, K Heitmann, S Habib, D Higdon Physical Review Letters 105 (24), 241302, 2010 | 226 | 2010 |
| Optimization under unknown constraints J Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith, ... Bayesian Statistics 9 (9), 229, 2011 | 199 | 2011 |
| Nonparametric reconstruction of the dark energy equation of state from diverse data sets T Holsclaw, U Alam, B Sansó, H Lee, K Heitmann, S Habib, D Higdon Physical Review D—Particles, Fields, Gravitation, and Cosmology 84 (8), 083501, 2011 | 176 | 2011 |
| 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 | 171 | 2016 |
| Multiscale modeling: a Bayesian perspective MAR Ferreira, HKH Lee Springer Science & Business Media, 2007 | 129 | 2007 |
| Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems JM Bernardo, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, ... Bayesian Statistics 7: Proceedings of the Seventh Valencia International …, 2003 | 126 | 2003 |
| Markov random field models for high-dimensional parameters in simulations of fluid flow in porous media HKH Lee, DM Higdon, Z Bi, MAR Ferreira, M West Technometrics 44 (3), 230-241, 2002 | 115 | 2002 |
| Bayesian guided pattern search for robust local optimization MA Taddy, HKH Lee, GA Gray, JD Griffin Technometrics 51 (4), 389-401, 2009 | 112 | 2009 |
| A Bayesian approach to characterizing uncertainty in inverse problems using coarse and fine-scale information D Higdon, H Lee, Z Bi IEEE Transactions on Signal Processing 50 (2), 389-399, 2002 | 103 | 2002 |
| Parameter space exploration with Gaussian process trees RB Gramacy, HKH Lee, WG Macready Proceedings of the twenty-first international conference on Machine learning, 45, 2004 | 93 | 2004 |
| Optimization subject to hidden constraints via statistical emulation H Lee, R Gramacy, C Linkletter, G Gray Pacific Journal of Optimization 7 (3), 467-478, 2011 | 76 | 2011 |
| Bayesian nonparametrics via neural networks HKH Lee Society for Industrial and Applied Mathematics, 2004 | 76 | 2004 |
| Surrogate models for mixed discrete-continuous variables LP Swiler, PD Hough, P Qian, X Xu, C Storlie, H Lee Constraint Programming and Decision Making, 181-202, 2014 | 67 | 2014 |
| Bagging and the Bayesian bootstrap M Clyde, H Lee International Workshop on Artificial Intelligence and Statistics, 57-62, 2001 | 65 | 2001 |
| Multi-scale and hidden resolution time series models MAR Ferreira, DM Higdon, HKH Lee, M West | 61* | 2006 |
| Gaussian processes and limiting linear models RB Gramacy, HKH Lee Computational Statistics & Data Analysis 53 (1), 123-136, 2008 | 60 | 2008 |
| Efficient models for correlated data via convolutions of intrinsic processes HKH Lee, DM Higdon, CA Calder, CH Holloman Statistical Modelling 5 (1), 53-74, 2005 | 57 | 2005 |
Robert B. GramacyVirginia TechVerified email at vt.edu
