Massively Parallel Approximate Gaussian Process Regression

DOI10.1137/130941912zbMath1308.62159arXiv1310.5182OpenAlexW2046916337MaRDI QIDQ2938459

Robert B. Gramacy, Robin M. Weiss, Jarad Niemi

Publication date: 14 January 2015

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1310.5182

zbMATH Keywords

emulatornonparametric regressioncluster computingbig datacomputer experimentgraphical processing unitsymmetric multiprocessor

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Nonparametric regression and quantile regression (62G08) Gaussian processes (60G15) Design of statistical experiments (62K99) Sequential statistical design (62L05) Sequential estimation (62L12)

Related Items (10)

Polar Gaussian Processes and Experimental Designs in Circular Domains ⋮ Unnamed Item ⋮ Large-scale local surrogate modeling of stochastic simulation experiments ⋮ Discussion of ``Saving storage in climate ensembles: a model-based stochastic approach ⋮ Calibrating a large computer experiment simulating radiative shock hydrodynamics ⋮ Scalable inference for space‐time Gaussian Cox processes ⋮ A case study competition among methods for analyzing large spatial data ⋮ Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments ⋮ Locally induced Gaussian processes for large-scale simulation experiments ⋮ Emulating Satellite Drag from Large Simulation Experiments

Uses Software

• CUDA
• laGP

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