A Python package for Bayesian regression.

The goal of this package is to be a relatively self-contained python package for Bayesian regression problems. The predominant focus of this package, for the time-being is on Gaussian Process (GP) models, loosely based on Carl Rasmussen's GPML toolbox in matlab, however the focus has shifted slightly as we have tried to generalize some of these methods.

The easiest way to install this package is by running

pip install -r https://github.com/mwhoffman/reggie/raw/master/requirements.txt
pip install git+https://github.com/mwhoffman/reggie.git

which will install the package and any of its dependencies. Once the package is installed the included demos can be run directly via python. For example, by running

python -m reggie.demos.basic

A full list of demos can be viewed here.