Citations (23)
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Seokhyun Chung & Raed Al Kontar. (2024) Federated Multi-Output Gaussian Processes. Technometrics 66:1, pages 90-103.
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Xia Cai, Li Xu, C. Devon Lin, Yili Hong & Xinwei Deng. (2024) Adaptive-region sequential design with quantitative and qualitative factors in application to HPC configuration. Journal of Quality Technology 56:1, pages 5-19.
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Dave Osthus, Brian P. Weaver, Lauren J. Beesley, Kelly R. Moran, Madeline A. Stricklin, Eric J. Zirnstein, Paul H. Janzen & Daniel B. Reisenfeld. (2023) Towards Improved Heliosphere Sky Map Estimation with Theseus. Technometrics 0:0, pages 1-19.
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Özge Sürer, Matthew Plumlee & Stefan M. Wild. (2023) Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models. Technometrics 0:0, pages 1-15.
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Annie Sauer, Andrew Cooper & Robert B. Gramacy. (2023) Vecchia-Approximated Deep Gaussian Processes for Computer Experiments. Journal of Computational and Graphical Statistics 32:3, pages 824-837.
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Deyu Ming, Daniel Williamson & Serge Guillas. (2023) Deep Gaussian Process Emulation using Stochastic Imputation. Technometrics 65:2, pages 150-161.
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Scott Koermer, Justin Loda, Aaron Noble & Robert B. Gramacy. Augmenting a simulation campaign for hybrid computer model and field data experiments. Technometrics 0:0, pages 1-20.
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Articles from other publishers (16)
Cesar Ruiz, Anshu Raj & Shuozhi Xu. (2024) Multivariate Gaussian process surrogates for predicting basic structural parameters of refractory non-dilute random alloys. APL Machine Learning 2:2.
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Alessia Benevento, Pouya Ahadi, Swati Gupta, Massimo Pacella & Kamran Paynabar. (2024)
Sequential sampling for functional estimation via
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Arnab Hazra, Pratik Nag, Rishikesh Yadav & Ying Sun. (2024) Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study. Journal of Agricultural, Biological and Environmental Statistics.
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Annie Sauer, Robert Gramacy & Ashwin Renganathan. (2024) Actively learning deep Gaussian process models for failure contour and probability estimation.. Actively learning deep Gaussian process models for failure contour and probability estimation..
Sidra Abbas, Gabriel Avelino Sampedro, Shtwai Alsubai, Stephen Ojo, Ahmad S. Almadhor, Abdullah Al Hejaili & Lubomira Strazovska. (2024) Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for Fine- Grained Heterogeneity Activity Recognition. IEEE Access 12, pages 44949-44959.
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Chih‐Li Sung & Rui Tuo. (2024) A review on computer model calibration. WIREs Computational Statistics 16:1.
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Mengrui Mina Jiang, Tanmay Khandait & Giulia Pedrielli. (2023) CGPT: A Conditional Gaussian Process Tree for Grey-Box Bayesian Optimization. CGPT: A Conditional Gaussian Process Tree for Grey-Box Bayesian Optimization.
Andrew Fowlie & Qiao Li. (2023) Modeling the R-ratio and hadronic contributions to $$g-2$$ with a Treed Gaussian process. The European Physical Journal C 83:10.
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Ryan B. Christianson, Ryan M. Pollyea & Robert B. Gramacy. (2023) Traditional kriging versus modern Gaussian processes for large‐scale mining data. Statistical Analysis and Data Mining: The ASA Data Science Journal 16:5, pages 488-506.
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Elham Yousefi, Luc Pronzato, Markus Hainy, Werner G. Müller & Henry P. Wynn. (2023) Discrimination between Gaussian process models: active learning and static constructions. Statistical Papers 64:4, pages 1275-1304.
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Ayao Ehara & Serge Guillas. (2023) AN ADAPTIVE STRATEGY FOR SEQUENTIAL DESIGNS OF MULTILEVEL COMPUTER EXPERIMENTS. International Journal for Uncertainty Quantification 13:4, pages 61-98.
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Jonathan Owen, Ian Vernon & Jonathan Carter. 2023. Bayesian Statistics, New Generations New Approaches. Bayesian Statistics, New Generations New Approaches
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Xinyi Fang, Mengyang Gu & Jianzhong Wu. (2022) Reliable emulation of complex functionals by active learning with error control. The Journal of Chemical Physics 157:21.
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Sébastien Marmin & Maurizio Filippone. (2022) Deep Gaussian Processes for Calibration of Computer Models (with Discussion). Bayesian Analysis 17:4.
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Raymundo Arróyave, Danial Khatamsaz, Brent Vela, Richard Couperthwaite, Abhilash Molkeri, Prashant Singh, Duane D. Johnson, Xiaoning Qian, Ankit Srivastava & Douglas Allaire. (2022) A perspective on Bayesian methods applied to materials discovery and design. MRS Communications 12:6, pages 1037-1049.
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Skyler D. Gray, Matthew J. Heaton, Dan S. Bolintineanu & Aaron Olson. (2022) On the Use of Deep Neural Networks for Large-Scale Spatial Prediction. Journal of Data Science, pages 493-511.
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