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    "info": {
        "author": "Tony Duan",
        "author_email": "tonyduan@cs.stanford.edu",
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            "License :: OSI Approved :: MIT License",
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        "description": "### Conjugate Bayesian Models\n\nLast update: June 2019.\n\n---\n\nLightweight Python library implementing a few conjugate Bayesian models. For details on the derivations see [1].\n\n```\npip3 install conjugate-bayes\n```\n\nWe support the following:\n\n#### To fit distribution models\n\n- Beta-Bernoulli\n- Gamma-Poisson\n- Normal-Inverse-Gamma\n\n#### To fit regression models\n\n- Linear regression with Normal Inverse-Gamma prior\n- Linear regression with Zellner's *g*-prior\n\n#### Future work\n\n- Dirichlet-Multinomial\n- Normal-Inverse-Wishart\n\n#### Usage\n\nBelow we show an example fitting a simple Bayesian linear regression with unknown beta and unknown variance.\n\n```python\nmodel = NIGLinearRegression(mu=np.zeros(2), v=100*np.eye(2), a=0.5, b=0.5)\nmodel.fit(x_tr, y_tr)\n\nsigma2 = model.get_marginal_sigma2()\nbeta = model.get_conditional_beta(sigma2=sigma2.mean())\n```\n\nThe above example results in the following prediction intervals.\n\n![ex_model](examples/ex.png)\n\nFor further details the `examples/` folder.\n\n#### References\n\n[1] P. D. Hoff, A First Course in Bayesian Statistical Methods (New York: Springer-Verlag, 2009).\n\n#### License\n\nThis library is available under the MIT License.\n\n",
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        "name": "conjugate-bayes",
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        "summary": "Conjugate Bayesian linear regression and distribution models in Python..",
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