bayesian-models 0.1.3

A package for building common bayesian models in pymc

bayesian-models

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)

Installation

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://[e-mail]/AlexRodis/bayesian-models.git

To install the developement version run:

pip install 'bayesian_models[dev]@ git+ssh://[e-mail]/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://[e-mail]/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