Note: This documentation has been moved to george.readthedocs.io.

George

George is a fast and flexible Python library for Gaussian Process 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).

George is being actively developed in a public repository on GitHub so if you have any trouble, open an issue there.

User Guide

• Getting started
  • Installation
  • A Simple Example
• Tutorial: model fitting with correlated noise
  • A Simple Mean Model
  • Simulated Dataset
  • Assuming White Noise
  • Modeling the Noise
  • The Final Fit
• Tutorial: setting the hyperparameters
  • Optimization
  • Sampling & Marginalization
• The GP object
• Kernels
  • Implementation Details
  • Basic Kernels
  • Radial Kernels
  • Periodic Kernels
  • Combining Kernels
  • Implementing New Kernels
• Solvers
  • Basic Solver
  • HODLR Solver

License & Attribution

Copyright 2014 Daniel Foreman-Mackey and contributors.

George is being developed by Dan Foreman-Mackey in a public GitHub repository. The source code is made available under the terms of the MIT license.

If you make use of this code, please cite the paper which is currently on the ArXiv:

@article{hodlr,
    author = {{Ambikasaran}, S. and {Foreman-Mackey}, D. and
              {Greengard}, L. and {Hogg}, D.~W. and {O'Neil}, M.},
     title = "{Fast Direct Methods for Gaussian Processes and the Analysis
               of NASA Kepler Mission Data}",
      year = 2014,
     month = mar,
       url = http://arxiv.org/abs/1403.6015
}