Skip to main content

Bayesian Logistic Regression using Laplace approximations to the posterior.

Project description

https://img.shields.io/travis/MaxPoint/bayes_logistic.svg https://img.shields.io/pypi/v/bayes_logistic.svg

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.

Demo

Example Notebook

History

0.2.0 (2015-09-02)

  • First release on PyPI.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page