-
1
-
-
0026471284
-
Efficient updating of kriging estimates and variances
-
Barnes, R. J., and Watson, A. (1992). Efficient Updating of Kriging Estimates and Variances. Mathematical Geology, 24, 129-133
-
(1992)
Mathematical Geology
, vol.24
, pp. 129-133
-
-
Barnes, R.J.1
Watson, A.2
-
2
-
-
34249069162
-
A framework for validation of computer models
-
Bayarri, M. J., Berger, J. O., Paulo, R., Sacks, J., Cafeo, J. A., Cavendish, J., Lin, C.-H., and Tu, J. (2007). A Framework for Validation of Computer Models. Technometrics, 49, 138-154
-
(2007)
Technometrics
, vol.49
, pp. 138-154
-
-
Bayarri, M.J.1
Berger, J.O.2
Paulo, R.3
Sacks, J.4
Cafeo, J.A.5
Cavendish, J.6
Lin, C.-H.7
Tu, J.8
-
3
-
-
85028094963
-
Sequential design of computer experiments for the estimation of a probability of failure
-
Bect, J., Ginsbourger, D., Li, L., Picheny, V., and Vazquez, E. (2012). Sequential Design of Computer Experiments for the Estimation of a Probability of Failure. Statistics and Computing, 22, 773-793
-
(2012)
Statistics and Computing
, vol.22
, pp. 773-793
-
-
Bect, J.1
Ginsbourger, D.2
Li, L.3
Picheny, V.4
Vazquez, E.5
-
4
-
-
84867871496
-
Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion. in Learning and Intelligent Optimization
-
Berlin, Heidelberg: Springer- Verlag
-
Benassi, R., Bect, J., and Vazquez, E. (2011). Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion. in Learning and Intelligent Optimization, Lecture Notes in Computer Science (Vol. 6683), Berlin, Heidelberg: Springer-Verlag, pp. 176-190
-
(2011)
Lecture Notes in Computer Science
, vol.6683
, pp. 176-190
-
-
Benassi, R.1
Bect, J.2
Vazquez, E.3
-
5
-
-
54949147327
-
Efficient global reliability analysis for nonlinear implicit performance functions
-
Bichon, B. J., Eldred, M. S., Swiler, L. P., Mahadevan, S., and McFarland, J. M. (2008). Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions. AIAA Journal, 46, 2459-2468
-
(2008)
AIAA Journal
, vol.46
, pp. 2459-2468
-
-
Bichon, B.J.1
Eldred, M.S.2
Swiler, L.P.3
Mahadevan, S.4
McFarland, J.M.5
-
6
-
-
84890900037
-
Fast computation of the multi-points expected improvement with applications in batch selection in learning and intelligent optimization
-
Chevalier, C., and Ginsbourger, D. (2013). Fast Computation of the Multi-Points Expected Improvement With Applications in Batch Selection. in Learning and Intelligent Optimization, Lecture Notes in Computer Science (Vol. 7997). pp. 59-69
-
(2013)
Lecture Notes in Computer Science
, vol.7997
, pp. 59-69
-
-
Chevalier, C.1
Ginsbourger, D.2
-
7
-
-
84918494632
-
Corrected kriging update formulae for batch-sequential data assimilation
-
Chevalier, C., Ginsbourger, D., and Emery, X. (2014). Corrected Kriging Update Formulae for Batch-Sequential Data Assimilation. Mathematics of Planet Earth, Lecture Notes in Earth System Sciences, pp. 119-122
-
(2014)
Mathematics of Planet Earth, Lecture Notes in Earth System Sciences
, pp. 119-122
-
-
Chevalier, C.1
Ginsbourger, D.2
Emery, X.3
-
8
-
-
84889096250
-
Kriginv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging
-
Chevalier, C., Picheny, V., and Ginsbourger, D. (2014). Kriginv: An Efficient and User-Friendly Implementation of Batch-Sequential Inversion Strategies Based on Kriging. Computational Statistics & Data Analysis, 71, 1021-1034
-
(2014)
Computational Statistics & Data Analysis
, vol.71
, pp. 1021-1034
-
-
Chevalier, C.1
Picheny, V.2
Ginsbourger, D.3
-
9
-
-
84863024315
-
-
in 6èmes Journé;es Nationales de Fiabilité; 24-26 Mars, Toulouse, France
-
Echard, B., Gayton, N., and Lemaire, M. (2010). Kriging-Based Monte Carlo Simulation to Compute the Probability of Failure Efficiently: AK-MCS Method. in 6èmes Journé;es Nationales de Fiabilité;, 24-26 Mars, Toulouse, France
-
(2010)
Kriging-Based Monte Carlo Simulation to Compute the Probability of Failure Efficiently: AK-MCS Method
-
-
Echard, B.1
Gayton, N.2
Lemaire, M.3
-
10
-
-
69249128063
-
The kriging update equations and their application to the selection of neighboring data
-
Emery, X. (2009). The Kriging Update Equations and Their Application to the Selection of Neighboring Data. Computational Geosciences, 13, 211-219
-
(2009)
Computational Geosciences
, vol.13
, pp. 211-219
-
-
Emery, X.1
-
11
-
-
31144474058
-
-
Boca Raton, FL: Chapman & Hall / CRC Press
-
Fang, K.-T., Li, R., and Sudjianto, A. (2006). Design and Modeling for Computer Experiments, Boca Raton, FL: Chapman & Hall / CRC Press
-
(2006)
Design and Modeling for Computer Experiments
-
-
Fang, K.-T.1
Li, R.2
Sudjianto, A.3
-
12
-
-
77955891392
-
-
Technical Report, Research Center PPE 2009
-
Finck, S., Hansen, N., Ros, R., and Auger, A. (2010). Real-Parameter Black-Box Optimization Bencharking 2009: Presentation of the Noiseless Functions. Technical Report, Research Center PPE, 2009
-
(2010)
Real-Parameter Black-Box Optimization Bencharking 2009: Presentation of the Noiseless Functions
-
-
Finck, S.1
Hansen, N.2
Ros, R.3
Auger, A.4
-
14
-
-
0030456906
-
The updated kriging variance and optimal sample design
-
Gao, H., Wang, J., and Zhao, P. (1996). The Updated Kriging Variance and Optimal Sample Design. Mathematical Geology, 28, 295-313
-
(1996)
Mathematical Geology
, vol.28
, pp. 295-313
-
-
Gao, H.1
Wang, J.2
Zhao, P.3
-
15
-
-
0001341675
-
Numerical computation of multivariate normal probabilities
-
Genz, A. (1992). Numerical Computation of Multivariate Normal Probabilities. Journal of Computational and Graphical Statistics, 1, 141-149
-
(1992)
Journal of Computational and Graphical Statistics
, vol.1
, pp. 141-149
-
-
Genz, A.1
-
16
-
-
79959416527
-
Kriging is well-suited to parallelize optimization
-
eds. L. M. Hiot, Y. S. Ong, Y. Tenne, and C.-K. Goh Springer
-
Ginsbourger, D., Le Riche, R., and Carraro, L. (2010). Kriging is Well-Suited to Parallelize Optimization. in Computational Intelligence in Expensive Optimization Problems, Adaptation Learning and Optimization (Vol. 2). eds. L. M. Hiot, Y. S. Ong, Y. Tenne, and C.-K. Goh, Springer, pp. 131-162
-
(2010)
Computational Intelligence in Expensive Optimization Problems Adaptation Learning and Optimization
, vol.2
, pp. 131-162
-
-
Ginsbourger, D.1
Le Riche, R.2
Carraro, L.3
-
17
-
-
65349171456
-
Adaptive design and analysis of supercomputer experiments
-
Gramacy, R., and Lee, H. (2009). Adaptive Design and Analysis of Supercomputer Experiments. Technometrics, 51, 130-145
-
(2009)
Technometrics
, vol.51
, pp. 130-145
-
-
Gramacy, R.1
Lee, H.2
-
18
-
-
79952811240
-
Particle learning of gaussian process models for sequential design and optimization
-
Gramacy, R. B., and Polson, N. G. (2011). Particle Learning of Gaussian Process Models for Sequential Design and Optimization. Journal of Computational and Graphical Statistics, 20, 102-118
-
(2011)
Journal of Computational and Graphical Statistics
, vol.20
, pp. 102-118
-
-
Gramacy, R.B.1
Polson, N.G.2
-
19
-
-
0035620460
-
Finding near-optimal bayesian experimental designs via genetic algorithms
-
Hamada, M., Martz, H., Reese, C., and Wilson, A. (2001). Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms. The American Statistician, 55, 175-181
-
(2001)
The American Statistician
, vol.55
, pp. 175-181
-
-
Hamada, M.1
Martz, H.2
Reese, C.3
Wilson, A.4
-
20
-
-
77955891392
-
-
Technical Report, INRIA 2009
-
Hansen, N., Finck, S., Ros, R., and Auger, A. (2010). Real-Parameter Black-Box Optimization Bencharking 2009: Noiseless Functions Definitions. Technical Report, INRIA 2009
-
(2010)
Real-Parameter Black-Box Optimization Bencharking 2009: Noiseless Functions Definitions
-
-
Hansen, N.1
Finck, S.2
Ros, R.3
Auger, A.4
-
21
-
-
0000561424
-
Efficient global optimization of expensive black-box functions
-
Jones, D. R., Schonlau, M., andWilliam, J. (1998). Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization, 13, 455-492
-
(1998)
Journal of Global Optimization
, vol.13
, pp. 455-492
-
-
Jones, D.R.1
Schonlau, M.2
William, M.J.3
-
24
-
-
77955204195
-
Adaptive designs of experiments for accurate approximation of target regions
-
Picheny, V., Ginsbourger, D., Roustant, O., Haftka, R. T., and Kim, N.-H. (2010). Adaptive Designs of Experiments for Accurate Approximation of Target Regions. Journal of Mechanical Design, 132
-
(2010)
Journal of Mechanical Design
, vol.132
-
-
Picheny, V.1
Ginsbourger, D.2
Roustant, O.3
Haftka, R.T.4
Kim, N.-H.5
-
25
-
-
54949098362
-
Sequential experiment design for contour estimation from complex computer codes
-
Ranjan, P., Bingham, D., and Michailidis, G. (2008). Sequential Experiment Design for Contour Estimation From Complex Computer Codes. Technometrics, 50, 527-541
-
(2008)
Technometrics
, vol.50
, pp. 527-541
-
-
Ranjan, P.1
Bingham, D.2
Michailidis, G.3
-
26
-
-
0001722811
-
Spatial designs
-
Sacks, J., and Schiller, S. (1988). Spatial Designs. Statistical Decision Theory and Related Topics IV, 2, 385-399
-
(1988)
Statistical Decision Theory and Related Topics
, vol.4
, Issue.2
, pp. 385-399
-
-
Sacks, J.1
Schiller, S.2
-
27
-
-
0024607804
-
Designs for computer experiments
-
Sacks, J., Schiller, S., and Welch, W. (1989a). Designs for Computer Experiments. Technometrics, 31, 41-47
-
(1989)
Technometrics
, vol.31
, pp. 41-47
-
-
Sacks, J.1
Schiller, S.2
Welch, W.3
-
28
-
-
84972517827
-
Design and analysis of computer experiments
-
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. (1989b). Design and Analysis of Computer Experiments. Statistical Science, 4, 409-435
-
(1989)
Statistical Science
, vol.4
, pp. 409-435
-
-
Sacks, J.1
Welch, W.J.2
Mitchell, T.J.3
Wynn, H.P.4
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