conjugate-bayes 0.0.1

Conjugate Bayesian Models

Last update: June 2019.

Lightweight Python library implementing a few conjugate Bayesian models. For details on the derivations see [1].

pip3 install conjugate-bayes

We support the following:

To fit distribution models

• Beta-Bernoulli
• Gamma-Poisson
• Normal-Inverse-Gamma

To fit regression models

• Linear regression with Normal Inverse-Gamma prior
• Linear regression with Zellner's g-prior

Future work

• Dirichlet-Multinomial
• Normal-Inverse-Wishart

Usage

Below we show an example fitting a simple Bayesian linear regression with unknown beta and unknown variance.

model = NIGLinearRegression(mu=np.zeros(2), v=100*np.eye(2), a=0.5, b=0.5)
model.fit(x_tr, y_tr)

sigma2 = model.get_marginal_sigma2()
beta = model.get_conditional_beta(sigma2=sigma2.mean())

The above example results in the following prediction intervals.

For further details the examples/ folder.

References

[1] P. D. Hoff, A First Course in Bayesian Statistical Methods (New York: Springer-Verlag, 2009).

License

This library is available under the MIT License.