bayeian-models is a small library build on top of pymc that
implements common statistical models
bayesian_models aims to implement sklearn style classes,
representing general types of models a user may wish to specify. Since
there is a very large variety of statistical models available, only some
are included in this library in a somewhat ad-hoc manner. The following
models are planned for implementation:
- BEST (Bayesian Estimation Superceeds the t Test) := Statistical comparisons' between groups, analoguous to hypothesis testing (COMPLETED)
bayesian-models can be installed with pip
pip install bayesian-models
Newer releases are first published to TestPyPI. They are installable as follows
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple bayesian-models
To install from git:
pip install git+ssh://git@github.com/AlexRodis/bayesian-models.git
To install the developement version run:
pip install 'bayesian_models[dev]@ git+ssh://git@github.com/AlexRodis/bayesian_models.git@dev-main'
It is often desirable to run models with a GPU if available. At present,
there are known issues with the numpyro dependency. Only these
versions are supported:
jax==0.4.1
jaxlib==0.4.1
To attempt to install with GPU support run:
pip install 'bayesian_models[GPU]@git+ssh://git@github.com/AlexRodis/bayesian-models.git'
Note: the GPU version is unstable
You must also set the following environment variable prior to all other commands, including imports
XLA_PYTHON_CLIENT_PREALLOCATE=false
These dependencies are only required with
pymc.sampling.jax.sample_numpyro_nuts and if using the default options
can be ignored