Gaussian Process Models for Computer Experiments With Qualitative and Quantitative Factors

279 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Dirichlet Process Mixtures of Generalized Linear Models (2009, background): “With the proper choice of covariance function, GPs can handle continuous and discrete covariates (Rasmussen and Williams, 2006; Qian et al., 2008).”
Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data (2009, methodology): “An alternative approach, using multivariate Gaussian processes to directly model the multivariate outputs, can be found in Qian, Wu, and Wu (2008) and in Conti and O’Hagan (2007).”
Review of multi-fidelity models (2016, methodology): “Subsequent research, including works by Qian et al. (2008) [174] and Biehler et al. (2015) [25], has explored the diverse applications of this methodology.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “This approach is designed under a GP modeling framework modified for both QT and QL variables (Qian et al. 2008).”
Managing computational complexity using surrogate models: a critical review (2020, methodology): “Cross validation is used to evaluate the outputs of a statistical study and generalize the results to other sets of data (Qian 2009; Qian et al. 2008, 2009a, b; Higdon et al. 2004; IT 2002; Jaeckel and Rebonato 1999; Reese et al. 2004; Shankar Bhattacharjee et al. 2016; Trucano et al. 2006; Kennedy…”