GPyReg

What is it?

GPyReg is a lightweight package for Gaussian process regression in Python. It was developed for use with PyVBMC (a Python package for efficient black-box Bayesian inference) but is usable as a standalone package.

Documentation

The documentation is currently hosted on github.io.

Installation

GPyReg is available via pip and conda-forge:

python -m pip install gpyreg

or:

conda install --channel=conda-forge gpyreg

GPyReg requires Python version 3.9 or newer.

Troubleshooting and contact

If you have trouble doing something with GPyReg, spot bugs or strange behavior, or you simply have some questions, please feel free to:

• Post in the lab's Discussions forum with questions or comments about GPyReg, your problems & applications;
• Open an issue on GitHub;
• Contact the project lead at luigi.acerbi@helsinki.fi, putting 'GPyReg' in the subject of the email.

You can also demonstrate your appreciation for GPyReg in the following ways:

• Star ⭐ the repository on GitHub;
• Follow Luigi Acerbi on X or Bluesky for updates about our other projects;

If you are interested in applications of Gaussian process regression to Bayesian inference and optimization, you may also want to check out PyVBMC for efficient black-box inference, and PyBADS, the Python implementation of Bayesian Adaptive Direct Search (BADS), our method for fast Bayesian optimization.

License

GPyReg is released under the terms of the BSD 3-Clause License.

Acknowledgments

GPyReg is developed by members (past and current) of the Machine and Human Intelligence Lab at the University of Helsinki and ELLIS Institute Finland. Development of GPyReg from version 1.1 onwards has been assisted by coding agents, including Anthropic's Claude Fable 5.1 and OpenAI's GPT-6 Astra. Work on the GPyReg package is supported by the Research Council of Finland (grants 356498 and 358980 to Luigi Acerbi) and its Flagship programme: Finnish Center for Artificial Intelligence FCAI.