Diagnostics for Gaussian Process Emulators

By Leonardo S. Bastos, Anthony OʼHagan. Technometrics, 2009

397 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/cf104e7608dc2550ccdb4d84efb12785c8a6fde6

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In Artificial Intelligence

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Citations in the corpus

Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) (2018, background): “The concept of fitting surrogate models is widely known in the computer experiments literature where it goes by the name of emulators (Bastos & O'Hagan, 2009).”
Considering covariates in the covariance structure of spatial processes (2011, methodology): “Bastos and O’Hagan (2009) mention that extreme values (large or small) of Dtð Þ indicate a conflict between the observed and predicted values.”
Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design (2019, methodology): “(cid:129) Ex1: (cid:129) Ex2 [52]: (cid:129) Ex3 [53]: (cid:129) Ex4 [54]: Tr ( ε ) , (cid:129) Ex5 [53]: For each example, two independent and unique datasets of size 30,000 are generated with Sobol sequence [50], where the ﬁ rst one is used for training and the second for validation.”
Explainable Neural Networks based on Additive Index Models (2018, methodology): “…models to help interpret a complex machine learning model is similar to the ﬁeld of computer experiments, where complicated computer simulations of physical systems are studied using well– understood statistical models, as described in Fang, Li & Sudjianto (2005) and Bastos & O’Hagan (2009).”
Cases for the nugget in modeling computer experiments (2010, methodology): “A metric based on the Mahalanobis distance, proposed by Bastos and O’Hagan (2009) for GP models, allows such correlation in the predicted outputs to be taken into account.”
RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY (2012, methodology): “Furthermore, from the cross-validation predictive errors and variances, one can compute some overall measures of error in order to assess the relevance of the model [20].”
Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2011, methodology): “Although several approaches have been explored in recent literature [e.g., Linkletter et al. (2006), Bastos and O’Hagan (2009), Yi, Shi and Choi (2011), and references therein], their complexity seems to have precluded the release of software for use by practitioners.”