Note: Check out my previous article for a practical discussion on why Bayesian modeling may be the right choice for your task. This tutorial will focus on a workflow + code walkthrough for building a…

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This guide provides a comprehensive workflow and code walkthrough for building a Bayesian linear regression model using STAN, a probabilistic programming language. It covers generating data, structuring the model string in STAN, specifying priors, and generating quantities for model evaluation. The tutorial emphasizes the importance of defining proper priors and model evaluation metrics like the WAIC score. It also hints at further exploration into Bayesian hierarchical models.

Bayesian Linear Regression: A Complete Beginner’s guide

<p>A great approach and tutorial on implementation of Bayesian modeling, but I wouldn’t say Complete Beginner. There are a fair number of assumptions to me made about the reader, including a basic understanding of linear algebra, and you make some assumptions about the reader’s understanding of some of the libraries that you use (matplotlib, numpy, etc). A great write-up, but I think you overestimate what a “Complete Beginner” might know :P</p>
