GPyReg Documentation

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.

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.

Documentation

The primary entry point for users is the GP Class, used to construct and fit Gaussian process models to data. More detailed information can be found in the links below:

Contents:

• Gaussian processes
  • gpyreg.gaussian_process
• Covariance functions
  • gpyreg.covariance_functions
• Mean functions
  • gpyreg.mean_functions
• Noise functions
  • gpyreg.noise_functions
• Slice sampling
  • gpyreg.slice_sample

Indices and tables

• Index

• Module Index

• Search Page

License and source

GPyReg is released under the terms of the BSD 3-Clause License. The source code is on GitHub.

Acknowledgments

GPyReg was developed by members (past and current) of the Machine and Human Intelligence Lab at the University of Helsinki. Development is being supported by the Finnish Center for Artificial Intelligence FCAI.