Introduction to Bayesian Linear Regression

The <a href="http://noahpinionblog.blogspot.com/2013/01/bayesian-vs-frequentist-is-there-any.html" rel="noopener ugc nofollow" target="_blank">Bayesian vs Frequentist debate</a> is one of those academic arguments that I find more interesting to watch than engage in. Rather than enthusiastically jump in on one side, I think it’s more productive to learn both methods of <a href="https://en.wikipedia.org/wiki/Statistical_inference" rel="noopener ugc nofollow" target="_blank">statistical inference</a> and apply them where appropriate. In that line of thinking, recently, I have been working to learn and apply Bayesian inference methods to supplement the frequentist statistics covered in my grad classes. One of my first areas of focus in applied Bayesian Inference was Bayesian Linear modeling. The most important part of the learning process might just be explaining an idea to others, and this post is my attempt to introduce the concept of Bayesian Linear Regression. We’ll do a brief review of the frequentist approach to linear regression, introduce the Bayesian interpretation, and look at some results applied to a simple dataset. I kept the code out of this article, but it can be found on <a href="https://github.com/WillKoehrsen/Data-Analysis/blob/master/bayesian_lr/Bayesian%20Linear%20Regression%20Demonstration.ipynb" rel="noopener ugc nofollow" target="_blank">GitHub in a Jupyter Notebook</a>. <a href="https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7">Click Here</a>