Enhancing response predictions with a joint Gaussian process model for stochastic simulation models
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Publication:6600076
DOI10.1145/3364219zbMath1544.62306MaRDI QIDQ6600076
Publication date: 8 September 2024
Published in: ACM Transactions on Modeling and Computer Simulation (Search for Journal in Brave)
Cites Work
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- Error estimation properties of Gaussian process models in stochastic simulations
- A note on L-estimates for linear models
- Robust quantile estimation and prediction for spatial processes
- Bayesian emulation of complex multi-output and dynamic computer models
- Constructing and fitting models for cokriging and multivariable spatial prediction
- Asymptotically optimum experimental designs for prediction of deterministic functions given derivative information
- Multivariate versus univariate Kriging metamodels for multi-response simulation models
- Stochastic simulation: Algorithms and analysis
- Efficient VaR and CVaR Measurement via Stochastic Kriging
- Stochastic Kriging for Simulation Metamodeling
- Simulating Sensitivities of Conditional Value at Risk
- Perspectives on the Evolution of Simulation
- A Batching Approach to Quantile Estimation in Regenerative Simulations
- The effects of common random numbers on stochastic kriging metamodels
- Bayesian Kriging Analysis and Design for Stochastic Simulations
- Confidence Intervals for Quantiles Using Sectioning When Applying Variance-Reduction Techniques
- Stochastic kriging with biased sample estimates
- Statistics for Spatial Data
- Enhancing Stochastic Kriging Metamodels with Gradient Estimators
- A Note on Quantiles in Large Samples
- Design and analysis of simulation experiments
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