Bayesian-validated surrogates for noisy computer simulations: application to random media
SIAM Journal on Scientific Computing
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
An efficient methodology for modeling complex computer codes with Gaussian processes
Computational Statistics & Data Analysis
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Stochastic Kriging for Simulation Metamodeling
Operations Research
Metamodel variability analysis combining bootstrapping and validation techniques
Proceedings of the Winter Simulation Conference
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The global sensitivity analysis method used to quantify the influence of uncertain input variables on the variability in numerical model responses has already been applied to deterministic computer codes; deterministic means here that the same set of input variables always gives the same output value. This paper proposes a global sensitivity analysis methodology for stochastic computer codes, for which the result of each code run is itself random. The framework of the joint modeling of the mean and dispersion of heteroscedastic data is used. To deal with the complexity of computer experiment outputs, nonparametric joint models are discussed and a new Gaussian process-based joint model is proposed. The relevance of these models is analyzed based upon two case studies. Results show that the joint modeling approach yields accurate sensitivity index estimators even when heteroscedasticity is strong.