10 Kriging: methods and applications
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Publication:3384281
DOI10.1515/9783110498967-010zbMath1480.93055OpenAlexW4206983105MaRDI QIDQ3384281
Publication date: 15 December 2021
Published in: System- and Data-Driven Methods and Algorithms (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1515/9783110498967-010
Cites Work
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- Comparison of Gaussian process modeling software
- Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments
- Privacy sets for constrained space-filling
- Robust optimization using computer experiments
- Efficient global optimization of expensive black-box functions
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- The design and analysis of computer experiments
- Analysis methods for computer experiments: how to assess and what counts?
- Robust Optimization in Simulation: Taguchi and Krige Combined
- Stochastic Kriging for Simulation Metamodeling
- Quantile Estimation with Latin Hypercube Sampling
- Stochastic intrinsic Kriging for simulation metamodeling
- Computer Experiments: Prediction Accuracy, Sample Size and Model Complexity Revisited
- Application-driven sequential designs for simulation experiments: Kriging metamodelling
- The First Approximation Algorithm for the Maximin Latin Hypercube Design Problem
- Improved Most Likely Heteroscedastic Gaussian Process Regression via Bayesian Residual Moment Estimator
- Generalized Integrated Brownian Fields for Simulation Metamodeling
- Some Monotonicity Results for Stochastic Kriging Metamodels in Sequential Settings
- Design and analysis of simulation experiments
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