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 license
Documentation: https://bayes_logistic.readthedocs.org.
Demo
History
0.2.0 (2015-09-02)
First release on PyPI.
Release files for bayes_logistic 0.2.0
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Release files / bayes_logistic-0.2.0.tar.gz
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