George

George is a fast and flexible Python library for Gaussian Process (GP) Regression. A full introduction to the theory of Gaussian Processes is beyond the scope of this documentation but the best resource is available for free online: Rasmussen & Williams (2006). Unlike some other GP implementations, george is focused on efficiently evaluating the marginalized likelihood of a dataset under a GP prior, even as this dataset gets Big™. As you’ll see in these pages of documentation, the module exposes quite a few other features but it is designed to be used alongside your favorite non-linear optimization or posterior inference library for the best results.

Keywords for this software

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References in zbMATH (referenced in 16 articles )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Andersen, Martin S.; Chen, Tianshi: Smoothing splines and rank structured matrices: revisiting the spline kernel (2020)
  2. Chen, Chuanfa; Li, Yanyan; Yan, Changqing: A random features-based method for interpolating digital terrain models with high efficiency (2020)
  3. Massei, Stefano; Robol, Leonardo; Kressner, Daniel: Hm-toolbox: MATLAB software for HODLR and HSS matrices (2020)
  4. Sushnikova, Daria A.; Oseledets, Ivan V.: Simple non-extensive sparsification of the hierarchical matrices (2020)
  5. Todescato, Marco; Carron, Andrea; Carli, Ruggero; Pillonetto, Gianluigi; Schenato, Luca: Efficient spatio-temporal Gaussian regression via Kalman filtering (2020)
  6. Bevilacqua, Moreno; Faouzi, Tarik; Furrer, Reinhard; Porcu, Emilio: Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics (2019)
  7. Bryson, Jennifer; Zhao, Hongkai; Zhong, Yimin: Intrinsic complexity and scaling laws: from random fields to random vectors (2019)
  8. Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.: Likelihood approximation with hierarchical matrices for large spatial datasets (2019)
  9. Mattos, César Lincoln C.; Barreto, Guilherme A.: A stochastic variational framework for recurrent Gaussian processes models (2019)
  10. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  11. Pang, Guofei; Yang, Liu; Karniadakis, George Em: Neural-net-induced Gaussian process regression for function approximation and PDE solution (2019)
  12. Park, Chiwoo; Apley, Daniel: Patchwork kriging for large-scale Gaussian process regression (2018)
  13. Minden, Victor; Damle, Anil; Ho, Kenneth L.; Ying, Lexing: Fast spatial Gaussian process maximum likelihood estimation via skeletonization factorizations (2017)
  14. Aminfar, AmirHossein; Ambikasaran, Sivaram; Darve, Eric: A fast block low-rank dense solver with applications to finite-element matrices (2016)
  15. Ballani, Jonas; Kressner, Daniel: Matrices with hierarchical low-rank structures (2016)
  16. Ho, Kenneth L.; Ying, Lexing: Hierarchical interpolative factorization for elliptic operators: integral equations (2016)