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Matthew Plumlee
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Year
Bayesian calibration of inexact computer models
M Plumlee
Journal of the American Statistical Association 112 (519), 1274-1285, 2017
1142017
Calibrating functional parameters in the ion channel models of cardiac cells
M Plumlee, VR Joseph, H Yang
Journal of the American Statistical Association 111 (514), 500-509, 2016
462016
Building Accurate Emulators for Stochastic Simulations via Quantile Kriging
M Plumlee, R Tuo
Technometrics 56 (4), 466-473, 2014
462014
Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics
DR Phillips, RJ Furnstahl, U Heinz, T Maiti, W Nazarewicz, FM Nunes, ...
Journal of Physics G: Nuclear and Particle Physics 48 (7), 072001, 2021
412021
Lifted Brownian kriging models
M Plumlee, DW Apley
Technometrics 59 (2), 165-177, 2017
372017
Fast prediction of deterministic functions using sparse grid experimental designs
M Plumlee
Journal of the American Statistical Association 109 (508), 1581-1591, 2014
352014
Orthogonal Gaussian process models
M Plumlee, VR Joseph
Statistica Sinica 28 (2), 601-619, 2018
202018
Computer model calibration with confidence and consistency
M Plumlee
Journal of the Royal Statistical Society: Series B 81 (3), 519-545, 2019
142019
Gaussian process modeling for engineered surfaces with applications to Si wafer production
M Plumlee, R Jin, V Roshan Joseph, J Shi
Stat 2 (1), 159-170, 2013
142013
Scalable adaptive batch sampling in simulation-based design with heteroscedastic noise
A van Beek, UF Ghumman, J Munshi, S Tao, TY Chien, ...
Journal of Mechanical Design 143 (3), 2021
122021
Multiresolution functional anova for large-scale, many-input computer experiments
CL Sung, W Wang, M Plumlee, B Haaland
Journal of the American Statistical Association 115 (530), 908-919, 2020
122020
Integration of normative decision-making and batch sampling for global metamodeling
A Van Beek, S Tao, M Plumlee, DW Apley, W Chen
Journal of Mechanical Design 142 (3), 031114, 2020
92020
High-fidelity hurricane surge forecasting using emulation and sequential experiments
M Plumlee, TG Asher, W Chang, MV Bilskie
The Annals of Applied Statistics 15 (1), 460-480, 2021
82021
Improving prediction from stochastic simulation via model discrepancy learning
H Lam, X Zhang, M Plumlee
2017 Winter Simulation Conference (WSC), 1808-1819, 2017
72017
Learning stochastic model discrepancy
M Plumlee, H Lam
2016 Winter Simulation Conference (WSC), 413-424, 2016
72016
Composite grid designs for adaptive computer experiments with fast inference
M Plumlee, CB Erickson, BE Ankenman, E Lawrence
Biometrika 108 (3), 749-755, 2021
62021
Revisiting subset selection
DJ Eckman, M Plumlee, BL Nelson
2020 Winter Simulation Conference (WSC), 2972-2983, 2020
62020
Plausible screening using functional properties for simulations with large solution spaces
DJ Eckman, M Plumlee, BL Nelson
Operations Research, 2022
52022
Improvements in storm surge surrogate modeling for synthetic storm parameterization, node condition classification and implementation to small size databases
AP Kyprioti, AA Taflanidis, M Plumlee, TG Asher, E Spiller, RA Luettich, ...
Natural Hazards 109 (2), 1349-1386, 2021
52021
Gradient based criteria for sequential design
CB Erickson, BE Ankenman, M Plumlee, SM Sanchez
2018 Winter Simulation Conference (WSC), 467-478, 2018
52018
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Articles 1–20