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Robert B. Gramacy
Researcher at Virginia Tech
Publications - 151
Citations - 7454
Robert B. Gramacy is an academic researcher from Virginia Tech. The author has contributed to research in topics: Gaussian process & Computer experiment. The author has an hindex of 43, co-authored 143 publications receiving 6225 citations. Previous affiliations of Robert B. Gramacy include Brigham Young University & University of South Florida.
Papers
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Journal ArticleDOI
Vecchia-approximated Deep Gaussian Processes for Computer Experiments
TL;DR: This work aims to bridge the gap by expanding the capabilities of Bayesian DGP posterior inference through the incorporation of the Vecchia approximation, allowing linear computational scaling without compromising accuracy or UQ.
Journal ArticleDOI
Bayesian treed response surface models
TL;DR: Recent advances in Bayesian modeling for trees are reviewed, from simple Bayesian CART models, treed Gaussian process, sequential inference via dynamic trees, to ensemble modeling via Bayesian additive regression trees (BART).
Proceedings Article
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
TL;DR: In this article, an alternative slack variable augmented Lagrangian (ALBO) is proposed to evaluate the expected improvement (EI) with library routines, and the slack variables furthermore facilitate equality as well as inequality constraints, and mixtures thereof.
Posted Content
Stochastic Simulators: An Overview with Opportunities
Evan Baker,Pierre Barbillon,Arindam Fadikar,Robert B. Gramacy,Radu Herbei,David Higdon,Jiangeng Huang,Leah R. Johnson,Pulong Ma,Anirban Mondal,Bianica Pires,Jerome Sacks,Vadim Sokolov +12 more
TL;DR: This review aims to bring a spotlight to the growing prevalence of stochastic computer models --- providing a catalogue of statistical methods for practitioners, an introductory view for statisticians, and an emphasis on open questions of relevance to practitioners and statisticians.
Journal ArticleDOI
Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models
TL;DR: In this article, a new and systematic Bayesian framework for simultaneous inference of the LRD and heavy-tailed distribution parameters of a parametric ARFIMA model with non-Gaussian innovations is presented.