├── README.md
└── src
    └── bayes-regression.ipynb


/README.md:
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1 | # Summary
2 | This tutorial presents the basic Bayesian approach to linear regression as developed in Bishop's [Pattern Recognition and Machine Learning](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?ie=UTF8&qid=1474997498&sr=8-1&keywords=pattern+recognition+and+machine+learning) text.  It follows his same approach and example, but provides the code in a jupyter notebook environment and my own explanations of the material.
3 | 
4 | The tutorial leverages a conjugate prior which leads to simple update equations for the posterior parameter distribution.  This is well suited for an environment of streaming data and online learning.
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6 | You can view an html rendering of the notebook [here](https://zjost.github.io/bayesian-linear-regression/).
7 | 


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