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Bayesian Logistic Regression using Laplace approximations to the posterior.

Project description

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.


Example Notebook


0.2.0 (2015-09-02)

  • First release on PyPI.

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