GUI-HDMR; A Software Tool for Global Sensitivity Analysis. GUI-HDMR is a freely available Matlab toolbox with a graphical user interface. The software provides a straightforward and efficient approach to explore the input-output mapping of a complex model with a large number of input parameters. Furthermore, variance based sensitivity indices can be determined in an automatic way in order to rank the importance of input parameters and to explore the influence of parameter interactions. The set of input values can be any Monte Carlo sample (if the input parameters can be controlled) or measured values (if using experimental data). If the input parameters can be controlled, then a quasi-random sampling method is preferable. This guarantees that the input space is covered more uniformly than by using random values and it provides a better convergence rate.

References in zbMATH (referenced in 22 articles )

Showing results 1 to 20 of 22.
Sorted by year (citations)

1 2 next

  1. Rathi, Amit Kumar; Chakraborty, Arunasis: Improved moving least square-based multiple dimension decomposition (MDD) technique for structural reliability analysis (2021)
  2. Qian, George; Mahdi, Adam: Sensitivity analysis methods in the biomedical sciences (2020)
  3. Borgonovo, Emanuele; Buzzard, Gregery T.; Wendell, Richard E.: A global tolerance approach to sensitivity analysis in linear programming (2018)
  4. Gündoğar, Zeynep; Demiralp, Metin: Block tridiagonal matrix enhanced multivariance products representation (BTMEMPR) (2018)
  5. Valkó, É.; Varga, T.; Tomlin, A. S.; Busai, Á.; Turányi, T.: Investigation of the effect of correlated uncertain rate parameters via the calculation of global and local sensitivity indices (2018)
  6. Cheng, Kai; Lu, Zhenzhou; Zhou, Yicheng; Shi, Yan; Wei, Yuhao: Global sensitivity analysis using support vector regression (2017)
  7. Lalande, Laure; Bourguignon, Laurent; Maire, Pascal; Goutelle, Sylvain: Mathematical modeling and systems pharmacology of tuberculosis: isoniazid as a case study (2016)
  8. Lambert, Romain S. C.; Lemke, Frank; Kucherenko, Sergei S.; Song, Shufang; Shah, Nilay: Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling (2016)
  9. Perdikaris, Paris; Venturi, Daniele; Karniadakis, George Em: Multifidelity information fusion algorithms for high-dimensional systems and massive data sets (2016)
  10. Beccacece, Francesca; Borgonovo, Emanuele; Buzzard, Greg; Cillo, Alessandra; Zionts, Stanley: Elicitation of multiattribute value functions through high dimensional model representations: monotonicity and interactions (2015)
  11. Karahoca, Adem; Tunga, M.: A polynomial based algorithm for detection of embolism (2015) ioport
  12. Tunga, Burcu; Demiralp, Metin: Weight optimization in HDMR with perturbation expansion method (2015)
  13. Gramacy, Robert B.; Taddy, Matt; Wild, Stefan M.: Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2013)
  14. Tunga, M. Alper; Demiralp, Metin: A novel method for multivariate data modelling: Piecewise Generalized EMPR (2013)
  15. Tunga, M. Alper; Demiralp, Metin: Bound analysis through HDMR for multivariate data modelling - CMMSE (2013)
  16. Özay, Evrim Korkmaz; Demiralp, Metin: Combined small scale high dimensional model representation (2012)
  17. Tunga, M. Alper; Demiralp, Metin: Multivariate data modelling through piecewise generalized HDMR method (2012)
  18. Wei, Pengfei; Lu, Zhenzhou; Hao, Wenrui; Feng, Jun; Wang, Bintuan: Efficient sampling methods for global reliability sensitivity analysis (2012)
  19. Tunga, Burcu; Demiralp, Metin: Fluctuation free multivariate integration based logarithmic HDMR in multivariate function representation (2011)
  20. Tunga, Burcu; Demiralp, Metin: Constancy maximization based weight optimization in high dimensional model representation for multivariate functions (2011)

1 2 next