========================= Bayes Logistic Regression

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This package will fit Bayesian logistic regression models with arbitrary prior means and covariance matrices, although we work with the inverse covariance matrix which is the log-likelihood Hessian.

Either the full Hessian or a diagonal approximation may be used.

Individual data points may be weighted in an arbitrary manner.

Finally, p-values on each fitted parameter may be calculated and this can be used for variable selection of sparse models.

• Free software (BSD): |lic|
• Documentation: https://bayes_logistic.readthedocs.org.
• See related presentation video here_.

.. |lic| image:: https://img.shields.io/github/license/MaxPoint/bayes_logistic.svg .. _here: http://www.opendatascience.com/conferences/rob-haslinger-at-bdf-2015-bayes_logistic-a-python-package-for-bayesian-logistic-regression/

Demo

Example Notebook_

.. _Example Notebook: http://nbviewer.ipython.org/github/MaxPoint/bayes_logistic/blob/master/notebooks/bayeslogistic_demo.ipynb