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    Marion Neumann [m dot neumann at wustl dot edu]
    Daniel Marthaler [dan dot marthaler at gmail dot com]
    Shan Huang [schan dot huang at gmail dot com]
    Kristian Kersting [kristian dot kersting at cs dot tu-dortmund dot de]

    This file is part of pyGPs.
    The software package is released under the BSD 2-Clause (FreeBSD) License.

    Copyright (c) by
    Marion Neumann, Daniel Marthaler, Shan Huang & Kristian Kersting, 18/02/2014

pyGPs is a Python library for Gaussian Process (GP) Regression and Classification. Here is an online documentation, where you will find a comprehensive introduction to functionalities and demonstrations. You can also find the same doc locally in /doc/build/html/index.html.

Generally speaking, pyGPs is an object-oriented GPs implementation. The functionality follows roughly the gpml matlab implementation by Carl Edward Rasmussen and Hannes Nickisch (Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2013-01-21). Standard GP regression and classification as well as FITC (sparse GPs) inference is implemented.

Further, pyGPs includes implementations of

  • implemented in python by Roland Memisevic 2008, following minimize.m which is copyright (C) 1999 - 2006, Carl Edward Rasmussen
  • (Copyright (c) Ian T Nabney (1996-2001))
  • (Copyright (c) by Hannes Nickisch 2010-01-10.)

Finally, pyGPs is constantly maintained. If you feel you have some relevant skills and are interested in contributing then please do get in touch. We appreciate any feedback.

Installing pyGPs

You can install via pip (Recommended!):

pip install pyGPs 

Alternatively, download the archive and extract it to any local directory. Install the package using

python install

or add the local directory to your PYTHONPATH:

export PYTHONPATH=$PYTHONPATH:/path/to/local/directory/../parent_folder_of_pyGPs


  • python 2.6 or 2.7 or NEW: python 3
  • scipy (v0.13.0 or later), numpy, and matplotlib: open-source packages for scientific computing using the Python programming language.


The following persons helped to improve this software: Roman Garnett, Maciej Kurek, Hannes Nickisch, Zhao Xu, and Alejandro Molina.

This work is partly supported by the Fraunhofer ATTRACT fellowship STREAM.


To cite pyGps, please use the following BibTex:

  author  = {Marion Neumann and Shan Huang and Daniel E. Marthaler and Kristian Kersting},
  title   = {pyGPs -- A Python Library for Gaussian Process Regression and Classification},
  journal = {Journal of Machine Learning Research},
  year    = {2015},
  volume  = {16},
  pages   = {2611-2616},
  url     = {}


pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.






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