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Journal of Computational and Graphical Statistics Volume 29, 2020 - Issue 2
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Scalable and Efficient Computation

Scalable Gaussian Process Computations Using Hierarchical Matrices

Christopher J. Geogaa Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL; Correspondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-5060-4799View further author information
ORCID Icon,
Mihai Anitescua Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL; ;b Department of Statistics, University of Chicago, Chicago, ILView further author information
&
Michael L. Steinb Department of Statistics, University of Chicago, Chicago, ILView further author information
Pages 227-237 | Received 20 Jul 2018, Accepted 18 Jul 2019, Published online: 06 Sep 2019

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Christopher J. Geoga & Michael L. Stein. (2023) A Scalable Method to Exploit Screening in Gaussian Process Models with Noise. Journal of Computational and Graphical Statistics 0:0, pages 1-11.
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Weichang Yu, Sara Wade, Howard D. Bondell & Lamiae Azizi. (2023) Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data. Journal of Computational and Graphical Statistics 32:2, pages 588-600.
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Tucker Hartland, Georg Stadler, Mauro Perego, Kim Liegeois & Noémi Petra. (2023) Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initialization. Inverse Problems 39:8, pages 085006.
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Yian Chen & Mihai Anitescu. (2023) Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices. SIAM/ASA Journal on Uncertainty Quantification 11:2, pages 682-725.
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Christopher J. Geoga, Oana Marin, Michel Schanen & Michael L. Stein. (2023) Fitting Matérn smoothness parameters using automatic differentiation. Statistics and Computing 33:2.
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Kelly R. Moran & Matthew W. Wheeler. (2022) Fast Increased Fidelity Samplers for Approximate Bayesian Gaussian Process Regression. Journal of the Royal Statistical Society Series B: Statistical Methodology 84:4, pages 1198-1228.
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Sagnik Mondal, Sameh Abdulah, Hatem Ltaief, Ying Sun, Marc G. Genton & David E. Keyes. (2022) Parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics. Parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics.
Sameh Abdulah, Qinglei Cao, Yu Pei, George Bosilca, Jack Dongarra, Marc G. Genton, David E. Keyes, Hatem Ltaief & Ying Sun. (2022) Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC. IEEE Transactions on Parallel and Distributed Systems 33:4, pages 964-976.
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Marcin Jurek & Matthias Katzfuss. (2022) Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering. Statistics and Computing 32:1.
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Amanda Lenzi, Julie Bessac & Mihai Anitescu. (2021) Power grid frequency prediction using spatiotemporal modeling. Statistical Analysis and Data Mining: The ASA Data Science Journal 14:6, pages 662-675.
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Christopher J. Geoga, Mihai Anitescu & Michael L. Stein. (2021) Flexible nonstationary spatiotemporal modeling of high‐frequency monitoring data. Environmetrics 32:5.
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Joseph Guinness. (2021) Gaussian process learning via Fisher scoring of Vecchia’s approximation. Statistics and Computing 31:3.
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Anant Mathur, Sarat Moka & Zdravko Botev. 2021. Performance Evaluation Methodologies and Tools. Performance Evaluation Methodologies and Tools 243 261 .
Qinglei Cao, Yu Pei, Kadir Akbudak, Aleksandr Mikhalev, George Bosilca, Hatem Ltaief, David Keyes & Jack Dongarra. (2020) Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications. Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications.
John Harlim, Daniel Sanz-Alonso & Ruiyi Yang. (2020) Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. SIAM/ASA Journal on Uncertainty Quantification 8:4, pages 1414-1445.
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