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Bayesian Logistic Regression using Laplace approximations to the posterior.

Project description

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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.

History

0.2.0 (2015-09-02)

  • First release on PyPI.

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