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.


References in zbMATH (referenced in 26 articles )

Showing results 1 to 20 of 26.
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  1. Guinness, Joseph: Nonparametric spectral methods for multivariate spatial and spatial-temporal data (2022)
  2. Jurek, Marcin; Katzfuss, Matthias: Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering (2022)
  3. Balcerek, Michał; Burnecki, Krzysztof; Sikora, Grzegorz; Wyłomańska, Agnieszka: Discriminating Gaussian processes via quadratic form statistics (2021)
  4. Jurek, Marcin; Katzfuss, Matthias: Multi-resolution filters for massive spatio-temporal data (2021)
  5. Ryan, John P.; Damle, Anil: Parallel skeletonization for integral equations in evolving multiply-connected domains (2021)
  6. Schäfer, Florian; Katzfuss, Matthias; Owhadi, Houman: Sparse Cholesky factorization by Kullback-Leibler minimization (2021)
  7. Schäfer, Florian; Sullivan, T. J.; Owhadi, Houman: Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity (2021)
  8. Andersen, Martin S.; Chen, Tianshi: Smoothing splines and rank structured matrices: revisiting the spline kernel (2020)
  9. Chen, Chuanfa; Li, Yanyan; Yan, Changqing: A random features-based method for interpolating digital terrain models with high efficiency (2020)
  10. Geoga, Christopher J.; Anitescu, Mihai; Stein, Michael L.: Scalable Gaussian process computations using hierarchical matrices (2020)
  11. Massei, Stefano; Robol, Leonardo; Kressner, Daniel: Hm-toolbox: MATLAB software for HODLR and HSS matrices (2020)
  12. Sushnikova, Daria A.; Oseledets, Ivan V.: Simple non-extensive sparsification of the hierarchical matrices (2020)
  13. Todescato, Marco; Carron, Andrea; Carli, Ruggero; Pillonetto, Gianluigi; Schenato, Luca: Efficient spatio-temporal Gaussian regression via Kalman filtering (2020)
  14. Bevilacqua, Moreno; Faouzi, Tarik; Furrer, Reinhard; Porcu, Emilio: Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics (2019)
  15. Bryson, Jennifer; Zhao, Hongkai; Zhong, Yimin: Intrinsic complexity and scaling laws: from random fields to random vectors (2019)
  16. Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.: Likelihood approximation with hierarchical matrices for large spatial datasets (2019)
  17. Mattos, César Lincoln C.; Barreto, Guilherme A.: A stochastic variational framework for recurrent Gaussian processes models (2019)
  18. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv
  19. Pang, Guofei; Yang, Liu; Karniadakis, George Em: Neural-net-induced Gaussian process regression for function approximation and PDE solution (2019)
  20. Genton, Marc G.; Keyes, David E.; Turkiyyah, George: Hierarchical decompositions for the computation of high-dimensional multivariate normal probabilities (2018)

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