Getting Started with Bayesian Regression
August 6, 2026
Bayesian Statistics is a branch of statistics that relies heavily on probability theory and statistical distributions. But beyond the mathematics, it also requires a good conceptual understanding of Bayesian thinking. I struggled with Bayesian analysis. I found myself getting overwhelmed by the mathematical calculations and, more importantly, had difficulty understanding how to translate a problem into a Bayesian framework.
In recent years, two books have helped me develop a much better conceptual understanding of Bayesian thinking. If you are interested in learning how to think in a Bayesian way, I would highly recommend reading these two books, along with the three YouTube videos below.
Books
- Everything Is Predictable: How Bayes' Remarkable Theorem Explains the World - Tom Chivers
- Bayesian Statistics for Beginners: A Step-by-Step Approach - Therese M. Donovan and Ruth M. Mickey
YouTube Videos
- https://youtu.be/R13BD8qKeTg?si=WeUDhwwgTlJAEMdr
- https://youtu.be/HZGCoVF3YvM?si=TsfjzEA4HrdGDDgS
- https://youtu.be/BrK7X_XlGB8?si=JxA2LRi8Vc8fvHVe
I have prepared a simple guide to performing regression analysis using Bayesian logic. Regression is one of the best practical ways to apply Bayesian thinking and understand its power. My goal was to create something practical that allows you to implement Bayesian regression without getting bogged down in complex mathematics. It is also a great way to see how Bayesian thinking can be applied to real-world data analysis.
This guide was prepared with the help of AI tools. These tools can generate incredibly useful content and can be excellent companions when learning something new. I hope this guide helps you take your first steps into the Bayesian regression world.
Happy Bayesian journey!
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