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Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning
10 references·1 citing·38 second hop
Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2013)Variable selection and sensitivity analys…Accurate emulators for large-scale computer experiments (2011)Accurate emulators for large-…Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models (2009)Implementation and evaluation…GUI–HDMR – A software tool for global sensitivity analysis of complex models (2008)GUI–HDMR – A software tool fo…Calculations of Sobol indices for the Gaussian process metamodel (2008)Calculations of Sobol indices…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Variable Selection for Gaussian Process Models in Computer Experiments (2006)Variable Selection for Gaussi…Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)Probabilistic Sensitivity Ana…The Design and Analysis of Computer Experiments (2003)The Design and Analysis of Co…On the Relative Importance of Input Factors in Mathematical Models (2002)On the Relative Importance of…Making best use of model evaluations to compute sensitivity indices (2002)Making best use of model eval…A taxonomy of constraints in black-box simulation-based optimization (2023)A taxonomy of constraints in …A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Design and Analysis of Computer Experiments (1989)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Computer experiments: a review (2010)Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning (2013)A NEW AND FLEXIBLE METHOD FOR CONSTRUCTING DESIGNS FOR COMPUTER EXPERIMENTS (2016)Statistics for Experimenters (1979)Factorial Sampling Plans for Preliminary Computational Experiments (1991)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Handbook on Constructing Composite Indicators (2005)Global sensitivity analysis using polynomial chaos expansions (2007)GIS and Multicriteria Decision Analysis (2000)Tools for Composite Indicators Building (2005)Uncertainty and Sensitivity Analysis Techniques as Tools for the Quality Assessment of Composite Indicators (2005)Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis (2012)STATISTICS FOR SPATIAL DATA (1992)Global Sensitivity Analysis for Systems with Independent and/or Correlated Inputs (2010)Mixed aleatory-epistemic uncertainty quantification with stochastic expansions and optimization-based interval estimation (2011)Asymptotic normality and efficiency of two Sobol index estimators (2013)Importance measures in global sensitivity analysis of nonlinear models (1996)A new uncertainty importance measure (2006)Bayesian analysis of computer code outputs: A tutorial (2006)How to avoid a perfunctory sensitivity analysis (2010)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Model emulation and moment-independent sensitivity analysis: An application to environmental modelling (2011)Sensitivity analysis methods in the biomedical sciences (2020)Surrogate-assisted global sensitivity analysis: an overview (2020)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)Building Accurate Emulators for Stochastic Simulations via Quantile Kriging (2013)Engineering-Driven Statistical Adjustment and Calibration (2014)Post-Fisherian Experimentation: From Physical to Virtual (2014)Making sense of global sensitivity analyses (2013)Global sensitivity analysis with dependence measures (2014)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)A Review on Global Sensitivity Analysis Methods (2015)Sensitivity analysis: A review of recent advances (2015)
this work references cited by second hop

References

  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 2002Making best use of model evaluations to compute sensitivity indices2,225 cited
  • 2003The Design and Analysis of Computer Experiments2,204 cited
  • 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach1,085 cited
  • 2002On the Relative Importance of Input Factors in Mathematical Models447 cited
  • 2008Calculations of Sobol indices for the Gaussian process metamodel331 cited
  • 2009Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models327 cited
  • 2008GUI–HDMR – A software tool for global sensitivity analysis of complex models210 cited
  • 2006Variable Selection for Gaussian Process Models in Computer Experiments167 cited
  • 2011Accurate emulators for large-scale computer experiments74 cited

Cited by

  • 2023A taxonomy of constraints in black-box simulation-based optimization34 cited

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