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Found 3 papers, 2 papers with code
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
Bayesian sequential design of computer experiments for quantile set inversion
We consider an unknown multivariate function representing a system-such as a complex numerical simulator-taking both deterministic and uncertain inputs.
Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs
This paper proposes new target sensitivity indices, based on the Shapley values and called "target Shapley effects", allowing for interpretable sensitivity measures under dependent inputs.
Statistics Theory Applications Methodology Statistics Theory
