This tutorial presents the basic Bayesian approach to linear regression as developed in Bishop's 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.
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.
You can view an html rendering of the notebook here.